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1
Xie, Ming 1973. "Prediction of daily net inflows for management of reservoir systems." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33043.
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Operational planning of water resource systems like reservoirs and hydropower plants calls for real-time forecasting of reservoir inflow. Reservoir daily inflow forecasts provide a warning of impending floods or drought conditions and help to optimize operating policies for reservoir management based on a fine time scale. The aim of this study was to determine the best model for daily reservoir inflow prediction through linear regression, exponential smoothing and artificial neural network (ANN) techniques. The Hedi reservoir, the third largest reservoir in south China with a 1.144 x 109 m 3, was selected as the study site. The performance of these forecasting models, in terms of forecasting accuracy, efficiency of model development and adaptability for future prediction, were compared to one another. All models performed well during the dry season (inflow with low variability), while the non-linear ANNs were superior to other models in frontal rainy season and typhoon season (inflow with high variability). The performance of ANN models were hardly affected by the high degree of uncertainty and variability inherent to the rainy season. Stepwise selection was very helpful in identifying significant variables for regression models and ANNs. This procedure reduced ANN's size and greatly improved forecasting accuracy for ANN models. The impact of training data series, model architecture and network internal parameters on ANNs performances were also addressed in this study. The overall evaluation indicates that ANNs are an effective and robust tool for input-output mapping under more extreme and variable conditions. ANNs provide an alternative forecasting approach to conventional time series forecasting models for daily reservoir inflow prediction.
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Dixon, Samuel G. "Seasonal forecasting of reservoir inflows in data sparse regions." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/33524.
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Management of large, transboundary river systems can be politically and strategically problematic. Accurate flow forecasting based on public domain data offers the potential for improved resource allocation and infrastructure management. This study investigates the scope for reservoir inflow forecasting in data sparse regions using public domain information. Four strategically important headwater reservoirs in Central Asia are used to pilot forecasting methodologies (Toktogul, Andijan and Kayrakkum in Kyrgyzstan and Nurek in Tajikistan). Two approaches are developed. First, statistical forecasting of monthly inflow is undertaken using relationships with satellite precipitation estimates as well as reanalysis precipitation and temperature products. Second, mean summer inflows to reservoirs are conditioned on the tercile of preceding winter large scale climate modes (El Niño Southern Oscillation, North Atlantic Oscillation, or Indian Ocean Dipole). The transferability of both approaches is evaluated through implementation to a basin in Morocco. A methodology for operationalising seasonal forecasts of inflows to Nurek reservoir in Tajikistan is also presented. The statistical models outperformed the long-term average mean monthly inflows into Toktogul and Andijan reservoirs at lead times of 1-4 months using operationally available predictors. Stratifying models to forecast monthly inflows for only summer months (April-September) improved skill over long term average mean monthly inflows. Individual months Niño 3.4 during October-January were significantly (p < 0.01) correlated to following mean summer inflows Toktogul, Andijan and Nurek reservoirs during the period 1941-1980. Significant differences (p < 0.01) occurred in summer inflows into all reservoirs following opposing phases of winter Niño 3.4 during the period 1941-1980. Over the period 1941-2016 (1993-1999 missing), there exists only a 22% chance of positive summer inflow anomalies into Nurek reservoir following November-December La Niña conditions. Cross validated model skill assessed using the Heidke Hit Proportion outperforms chance, with a hit rate of 51-59% depending upon the period of record used. This climate mode forecasting approach could be extended to natural hazards (e.g. avalanches and mudflows) or to facilitate regional electricity hedging (between neighbouring countries experiencing reduced/increased demand). Further research is needed to evaluate the potential for forecasting winter energy demand, potentially reducing the impact of winter energy crises across the region.
3
Westra, Seth Pieter Civil & Environmental Engineering Faculty of Engineering UNSW. "Probabilistic forecasting of multivariate seasonal reservoir inflows: accounting for spatial and temporal variability." Awarded by:University of New South Wales. Civil & Environmental Engineering, 2007. http://handle.unsw.edu.au/1959.4/40630.
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Hydrological variables such as rainfall and streamfiow vary at a range of temporal scales, from short term (diurnal and seasonal) to the inter annual time scales associated with the El Nino - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phenomena, to even longer time scales such as those linked to the Pacific (inter-) Decadal Oscillation (PDO). This temporal variability poses a significant challenge to hydrologists and water resource managers, since a failure to take such variability into account can lead to an underestimation of the likelihood of droughts and sequences of above average rainfall, which in turn has important implications for the design and operation of reservoirs for hydroelectricity generation, irrigation and municipal water supply. Understanding and accounting for this variability through well designed prediction systems is thus an important part of improving the planning, management and operation of complex water resources systems. This thesis outlines the application of two statistical techniques: wavelets and independent component analysis, to identify sources of hydrological variability, and then use this information to probabilistically generate multivariate seasonal forecasts or develop extended synthetic sequences of hydrological time series. The research is divided into four main parts. The first part outlines an application of the method of wavelets to analyse sources of Australian rainfall variability, and shows that there are coherent regions of variability in addition to the ENSO phenomenon that should be considered when developing seasonal forecasts. The second part examines the capability of three component extraction techniques: principal component analysis (PCA), Varimax and independent component analysis (ICA), in identifying and interpreting modes of variability in the global sea surface temperature dataset. The third part outlines a new technique that uses ICA to factorise multivariate reservoir inflow time series into a set of independent univariate time series, so that univariate methods can be used to develop multivariate synthetic sequences and probabilistic seasonal forecasts. Finally, the fourth part synthesises the previous three parts by demonstrating a wavelets- and correlation-based methodology for assessing sources of climate variability, and then using ICA to generate probabilistic multivariate seasonal forecasts of reservoir inflows that form part of Sydney's water supply system.
4
Del, Castillo Maravi Yanil. "New inflow performance relationships for gas condensate reservoirs." Texas A&M University, 2003. http://hdl.handle.net/1969/354.
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Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29800.
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In practice, the reservoir net inflow is computed based upon the application of the water balance equation to the reservoir system since it is difficult to obtain direct and reliable measurements of this variable. The net inflow process has been thus found to possess a random behaviour because it is related to the stochastic nature of various physical processes involved in the water balance computation (e.g., precipitation, evaporation, etc.). Therefore, the aim of this research is to propose a forecasting method that can accurately and efficiently predict the random reservoir inflow series. The proposed forecasting methods considered were the linear regression, the exponential smoothing technique, the periodic autoregressive moving average (PARMA) method, and the neural network procedure. An illustrative application was carried out using 25 years (1970--1994) of monthly rainfall and inflow data from the Pedu-Muda reservoirs in Kedah, Malaysia. The first 18 years (1970--1987) were used for calibration while the remaining 7 years (1988--1994) were used for verification of the proposed models. (Abstract shortened by UMI.)
6
Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/MQ54220.pdf.
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Zaman, Md Sazid. "Decision analysis framework for high inflow events for small hydropower reservoir systems." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/28036.
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Hydro system operators are often confronted with a myriad of conflicting and challenging decision situations. In particular, managing hydroelectric facilities during high inflow or unusual events can be complex, time consuming and challenging. Most high inflow events that challenge operational planners are driven by hydrology, with either too much or too little water being available. Other factors such as unusual electricity market conditions, dam safety or equipment concerns also drive decision making.
In a typical case operators try to balance multiple, and at times, competing objectives during high inflow events. In the case of high inflow subject flood events, Operation Planning Engineers are usually under time pressure to make decisions when the potential outcomes of different management options are highly uncertain. In such situations, planners must quickly make critical and important decisions taking into account the current state of the system and latest available information and forecasts. Their decisions can have environmental, social and financial consequences.
The purpose of this research is to develop an effective tool for the Operation Planning Engineers in Generation Resource Management of BC Hydro (British Columbia Hydro and Power Authority), which can be quickly and efficiently used during high inflow events at some of BC Hydro facilities. We describe the process that we have developed to build a tool to implement a Structured Decision Making Framework for a typical BC Hydro facility. The tool addresses the inflow uncertainties associated with high inflow floods and includes multiple objectives that are difficult to measure by means of a common unit, which necessitated the development of utility functions and required a trade-off analysis to be carried out. In this paper we also describe a methodology to do the tradeoff analysis among the objectives.
We present the results of the analysis for a flood event in the Cheakamus River, October, 2003. At the end of the project decisions made in real-time will be less dependent on the planner’s own risk tolerance and more aligned with corporate risk tolerances that are acceptable to senior management.
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Barnard, Joanna Mary. "The value of inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/27759.
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The present study examines the value of conceptual hydrologic forecasting in the operation of a hydroelectric generating project. The conceptual forecasting method used is the UBC Watershed Model. The value of the conceptual forecast is determined by comparing results obtained by use of the forecast to those obtained by use of a forecast based purely on the historic record. The effect of the size of the reservoir on the value of the forecast is also considered.
The operation of a hypothetical project is modelled using dynamic programming. The operation of the project is optimized using the conceptual and historic forecasts to generate a variety of operating policies. The operation of the project is then simulated using the derived operating policies and several test years of real data, to determine the potential energy generation for each scenario. The analysis is performed for several reservoir sizes and for deterministic and two stochastic representations of the data.
The analysis concludes that conceptual forecasting is most useful when the annual flow is significantly different from the average annual flow of the basin. If an historic forecast is used, a deterministic representation of the data is most valuable. If a conceptual forecast is used stochastic analysis gives the most efficient operation. Forecasting of either kind is valuable for reservoir sizes greater than 25% of the mean annual flow, but the value decreases as the volume approaches 100% of the mean annual flow.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
9
Faraca, Lee Joon. "A WATER BALANCE AND SEDIMENT YIELD ANALYSIS MODEL FOR THE LOPEZ LAKE RESERVOIR." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2203.
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Lopez Lake Reservoir is the primary source of potable water for the Cities of Arroyo Grande, Grover Beach, Pismo Beach, and to the Community Service Districts of Oceano and Avila Beach. In this study, a water balance and sediment yield analysis model was developed for the reservoir’s watershed. The model was used to estimate evaporation from the lake and to examine the effects of a wildfire on the reservoir. Evaporation and wildfire are dependent on variables that change on a spatial and temporal scale, making modeling challenging. The County of San Luis Obispo uses pan coefficients to estimate evapotranspiration losses from the reservoir. In this study, a water balance model was developed using a watershed model known as Soil and Water Assessment Tool, SWAT. Evaporation loss from the lake was calculated using the inflows simulated by the model, and other fluxes (e.g., water released for consumption to Arroyo Grande Creek, precipitation) that were obtained from the County of San Luis Obispo. The evaporation values estimated by the pan coefficient model were significantly higher than the water balance and the Penman-Monteith predictions. The Penman-Monteith method estimates seem more reasonable for the lake. SWAT was also used to simulate effects of a wildfire on sediment inflow and sediment yield into the reservoir for a year after a simulated fire. Results showed that sediment inflow rates increased by a factor of 3 following the simulated wildfire. Lopez Lake Reservoir’s capacity would be significantly affected by a wildfire. To improve the evaporation estimates it is recommended that the County of San Luis Obispo install streamflow gauges to measure the inflow into the reservoir. Using the streamflow gauges the reservoir evaporation could be calculated using the water balance method. Adding climate gauges at the reservoir would increase the accuracy of the Penman-Monteith method. Sediment gauges in the watershed would provide a calibration data source for the model as well as data collection points in the event of an actual wildfire.
10
Bourdin, Dominique R. "A probabilistic inflow forecasting system for operation of hydroelectric reservoirs in complex terrain." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45173.
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This dissertation presents a reliable probabilistic forecasting system designed to predict inflows to hydroelectric reservoirs. Forecasts are derived from a Member-to-Member (M2M) ensemble in which an ensemble of distributed hydrologic models is driven by the gridded output of an ensemble of numerical weather prediction (NWP) models. Multiple parameter sets for each hydrologic model are optimized using objective functions that favour different aspects of forecast performance. On each forecast day, initial conditions for each differently-optimized hydrologic model are updated using meteorological observations. Thus, the M2M ensemble explicitly samples inflow forecast uncertainty caused by errors in the hydrologic models, their parameterizations, and in the initial and boundary conditions (i.e., meteorological data) used to drive the model forecasts.
Bias is removed from the individual ensemble members using a simple degree-of-mass-balance bias correction scheme. The M2M ensemble is then transformed into a probabilistic inflow forecast by applying appropriate uncertainty models during different seasons of the water year. The uncertainty models apply ensemble model output statistics to correct for deficiencies in M2M spread. Further improvement is found after applying a probability calibration scheme that amounts to a re-labelling of forecast probabilities based on past performance.
Each component of the M2M ensemble has an associated cost in terms of time and/or money. The relative value of each ensemble component is assessed by removing it from the ensemble and comparing the economic gains associated with the reduced ensembles to those achieved using the full M2M system. Relative value is computed using a simple (static) cost-loss decision model in which the reservoir operator takes action (lowers the reservoir level) when significant inflows are predicted with probability exceeding some threshold.
The probabilistic reservoir inflow forecasting system developed in this dissertation is applied to the Daisy Lake hydroelectric reservoir located in the complex terrain of southwestern British Columbia, Canada. The hydroclimatic regime of the case study watershed is such that flashy fall and winter inflows are driven by Pacific frontal systems, while spring and summer inflows are dominated by snow and glacier melt. Various aspects of ensemble and probabilistic forecast performance are evaluated over a period of three water years.
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Zhou, Dequan. "The value of one month ahead inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30145.
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The research assesses the value of forecast information in operating a hydro-electric project with a storage reservoir. The benefits are the increased hydro power production,
when forecasts are available. The value of short term forecasts is determined by comparing results obtained with the use of one month ahead perfect predictions to those obtained without forecasts but a knowledge of the statistics of the possible flows. The benefits with perfect forecasts provide an upper limit to the benefits which could be obtained with actual less than perfect forecasts. The effects of generating capacity and flow patterns are also discussed.
The operation of a hypothetical but typical project is modelled using stochastic dynamic
programming. A simple model of streamflow is formulated based on the historical statistics ( means and deviations).
The conclusions are: The inflow forecasts can improve the operational efficiency of the reservoir considerably because of the reduction in forecasting uncertainty. The maximum release constraints affect the additional expected values. The benefits from the forecasts increase as the discharge limits reduce. Flow predictions in the high flow season are most valuable when the runoff in that time period dominates the annual flow pattern. However flow predictions at other times of the year also have value.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
12
Rasmussen, Ryan. "Investigation of stochastic optimization methods for operating reservoirs with snowmelt-dominant local inflows and limited storage capability in British Columbia during the spring freshet." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51558.
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The reservoir operations model developed in this thesis is a stochastic dynamic programming decision support tool for the optimization of the operation of snowmelt-driven reservoirs with small storage flexibility hydropower systems during the spring freshet. The model operates under the objective of maximizing the value of electricity generation through electricity trading over a short-term planning period. Project and watershed data, stochastic inflows, and estimated electricity prices are used to calculate optimal expected turbine release policies over a short-term planning period. Results are used to provide decision support to operators in the form of a daily expected optimal turbine release volume and marginal value of energy of the reservoir. Including stochasticity in the model allows for inflow probabilities, which may not be easily evaluated by an operator, to be reflected in an operation decision. A combination of forecast, historical, and current state of the system data is included in the model to reflect the most up-to-date view of uncertain conditions. Case studies indicate that although operators may deviate from the expected optimal policy to meet other interests and requirements in real-time, the model provides an optimal expected policy during the freshet period and has shown in a case study to increase the value of a single reservoir’s operations by 6% during one three-month freshet period.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
13
Alipour, Mohammadhossein. "Applying the virtual structure of a risk-informed decision making framework for operating small hydropower reservoirs during high inflow events, case study : Cheakamus River system." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43587.
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Operating hydropower reservoirs with small storage capacity is a challenging task due to the fact that in a watershed system there usually exist multiple stakeholders with different and conflicting preferences and values. Consequently the process of planning for reservoir operation must be carried out with consideration of several, usually competing, objectives. This process becomes even more challenging during a high inflow or flooding event for three main reasons. First, the objective of minimizing adverse consequences of such an event is added to the set of objectives that the operator must deal with. Second, inflow forecast uncertainty-driven risks are highly intensified due to the high sensitivity of the outcomes to inflow forecasts. And third, the available time for making a decision is very short while comprehensive analysis is a necessity in order to make an informed decision regarding the best operational alternative. Under these circumstances, the best approach to confront this challenge could be developing a Risk-Informed Decision Making (RIDM) framework that provides operation planning engineers with a solid and pre-designed guideline to deal with the task of identifying the best operational alternative in an efficient and timely manner.
The current study is an attempt to apply the virtual structure of a RIDM framework for the Cheakamus River system in British Columbia. The framework is a coherent assembly of a number of methods and tools we have either developed or utilized from the existing widely used methods and techniques in practice. The product of our work is an example of the necessary tools that need to be used to develop recommendations for operating Daisy Lake reservoir during a high inflow event in a manner that all the operational objectives are served in the best possible way. This is done while taking into account making trade-offs among competing objectives. We illustrate the practical applicability and merits of the framework through applying it to a historical high inflow period in October 2003. The outcome is near real-time decisions with less dependency on only planners’ judgement and more dependency on thorough and systematic analysis with consideration of human judgement and possible risk tolerances.
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Sázel, Jiří. "Střednědobé předpovědi průtoků vody v měrném profilu toku." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2015. http://www.nusl.cz/ntk/nusl-234548.
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Thesis is aimed on creation of prediction model for releasing medium-term water stream flow forecasts. Created model create forecasts based on principal of finding most similar historical case. Usefulness of forecasting model is demonstrated for operation of one isolated reservoir in gauge profile Oslavany on river Oslava.
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Signoriello, Giuseppe Alessandro 1977. "Modelos matemáticos para previsão de vazões afluentes à aproveitamentos hidrelétricos." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265912.
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Orientador: Ieda Geriberto HidalgoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-25T19:15:52Z (GMT). No. of bitstreams: 1 Signoriello_GiuseppeAlessandro_M.pdf: 31629174 bytes, checksum: 1674c1adcccf93d9b3ee9711be3f709e (MD5) Previous issue date: 2014
Resumo: Este trabalho apresenta a comparação de dois modelos matemáticos desenvolvidos para prever vazões afluentes à usinas hidrelétricas. O objetivo é abordar os aspectos que determinam a qualidade do insumo fundamental para a programação da operação do sistema hidrotérmico brasileiro: a previsão de vazões. A ferramenta de suporte à avaliação dos modelos matemáticos é o SISPREV, gerenciador/executor de estudos de previsão de vazões desenvolvido na UNICAMP. Esta ferramenta permite ao usuário prever vazões diárias e mensais com base em modelos de Regressão Linear (RL) e Sistema de Inferência Neuro-Fuzzy (SINF). Avaliou-se a qualidade das previsões diárias e mensais dos modelos RL e SINF através da metodologia de mineração de dados Cross Industry Standard Process for Data Mining (CRISP-DM). A CRISP-DM é baseada em um modelo hierárquico de processos comumente usados na descoberta de conhecimento. Os resultados mostram que o modelo RL apresenta um desempenho melhor para previsões diárias e o modelo SINF para as previsões mensais. Além disso, o modelo RL tem a tendência a ter bom desempenho de previsão nas situações típicas de chuva-vazão, enquanto os melhores índices de desempenho do modelo SINF caem nas condições atípicas, em particular com a contemporaneidade de altas vazões e baixas precipitações
Abstract: This work presents a comparison between two different mathematical models developed to predict inflows to hydropower plants. The purpose is to explore the aspects that determine the quality of an important input variable for operation planning of the Brazilian hydrothermal system: the inflows forecasting. The tool that supports the evaluation of the mathematical models is called SISPREV. It is a manager/runner of inflows forecasting studies developed at UNICAMP. This tool allows the user to predict daily and monthly inflows based on Linear Regression (RL) models and Neuro-Fuzzy Inference System (SINF). In this thesis, was evaluated the quality of daily and monthly forecasts of RL and SINF models using the methodology Cross Industry Standard Process for Data Mining. CRISP-DM is used in the discovery of knowledge and based on a hierarchical process model. The results show that the RL model performs better for daily predictions and the SINF model for monthly predictions. Furthermore, the RL model tends to have better performance in typical situations of rainfall-inflow, while the best performance indices of the SINF model fall in atypical conditions, in particular with the simultaneous high inflow rates and low precipitation
Mestrado
Planejamento de Sistemas Energeticos
Mestre em Planejamento de Sistemas Energéticos
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Han, Wan-rong, and 韓宛容. "Apply Statistical-Downscaling Climate Forecasts for Estimating Shihmen Reservoir Inflows." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/66191775238781910522.
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碩士國立中央大學
水文與海洋科學研究所
100
Resources in Taiwan not only are impotant for water resources management, but also paly as retention measures against flooding. In recent years, the need for domestic and industrial water have increased rapidly because of economic vigorous development, which result in rising stress of water supply especially in drought periods. Therefore, if reservoir inflows can be quantitatively forecasted beforehand, it will be helpful for issuing drought wqrning and making properly decision for water allocations. The Central Weather Bureau (CWB) issued short-term climate forecasts by statistical downscaling for precipitation and temperature with lead time of 5 months in a 1-month moving window. The objective of this study is to apply the short-term climate forecasts by integrating with a weather generator and a watershed hydrological model to predict inflows of the Shihmen Reservoir with the maximum lead time of 5 months. Both probabilistic flow forecasts and deterministic flow forecasts were produced in this approach, as well as the associated potential economic values of two flow forecasts. The sampling techniques, including maximum probability, weighted probability, and bias correction probability, were applied to retrieve monthoy mean values of precipitation and temperature from the climate forecast. Then a weather generator was applied to generate daily temperature and precipitation to drive a hydrological model for inflow predictions of the Shimen Reservoir. The skill scores (RPSS, LEPS and MSE) of three sampling results were all greater than climatology skill. The maximum probability approach has the highest predictive ability. Results of both probabilistic flow forecasts and deterministic flow forecasts are also greater than climatology skill, and show certain economic values from June to October. From January to May, the deterministic flow forecasts possess greater economic benefits than that of the probabilistic flow forecasts for cases of observed inflows at blow normal outlooks; while, the probabilistic flow forecasts possess greater economic benefits that than of the deterministic flow forecasts for cases of observed inflows at above normal outlooks.
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Tong, Hsin-ju, and 童新茹. "Linking Seasonal Climate Outlooks and Hydrological Models for Estimating Shihmen Reservoir Inflows." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/75034906933941375662.
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碩士國立中央大學
水文與海洋科學研究所
99
The water resource is indispensable for human. The occurrence of drought event would cause damage to agriculture, industry, and economy. The impact of drought is really affect people. However, drought develops slowly and imperceptibly and may remain unnoticed for a long time. So it is very hard to construct a drought early warning system. While drought coming, it is too late to make the best decision. For these reasons, if we can predict the future reservoir inflow may manage the water resource earlier and reduce the impact of drought. The Central Weather Bureau (CWB) issued the seasonal climate forecast every month. The CWB seasonal climate forecast provide for precipitation and temperature as percentage likelihood with lead time of 3 months in 1-month moving windows. The objection of this study is to apply the product of CWB seasonal forecast, and use weather generator to derived daily weather series such as precipitation and temperature. Since the daily weather series were derived by weather generators, the 3 months inflow of Shihmen reservoir were further simulated by a physical hydrological model. Two weather generators were selected to derived daily weather series in this study, which are weather generator (WGEN, Tung and Haith, 1995) and Semiparametric Weather Generator (SWG, Apipattanavis et al., 2007). The simulation results indicate that WGEN capture the historical mean is better than SWG. SWG shows a slight underestimate to historical mean. Use these derived daily weather series to simulate Shihmen reservoir’s inflow. The simulation results indicate that the two kinds of weather generators predicted the inflow well, but when the seasonal climate forecast is not accurate it would affect the simulation’s accuracy.
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Chang, Ting-Wing, and 張廷暐. "Evaluate the Climate Change Impact on the Inflows of the Shihmen Reservoir." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/v3h2w4.
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碩士國立中央大學
水文所
96
The enhancement of greenhouse effect has seriously affected the earth’s climate system, especially the issue of global warming. This warming is accompanied by significant changes in the hydrological cycles which subsequently bring impacts on water resources and increase difficulties on water resources allocations. The Shihmen Reservoir is one of the most important reservoirs in northern Taiwan. It provides water supplies to agricultural, industries, and domestics of the Taipei, Taoyuan, and Hsinchu counties, and plays an important role of flood amd drought mitigations for these areas. The purpose of this study is to evaluate the climate change impact on the inflows of the Shihmen reservoir and the changes in hydrological cycles of the watershed. The results can be applied for the planning of adaption and mitigation to reduce the climate change impact on water resources in northern Taiwan. Historical hydrological and meteorological data are analyzed to reveal characteristics and possible trends of climate in Shihmen area. The amounts of rainfalls between April to October account for 57 % of annual rainfalls. The rainfall intensity has gradually increased at a rate of 0.067~0.254 mm/day. A raise in temperature was found around 0.018~0.34 oC/10-yr. The runoff ratio has a decrease trend of0.009/10-yr. The climate change scenarios were mainly from outputs of two global circulation models, CGCM2 and HADCM3. The reservoir inflows were simulated by a water balance model, GWLF (Generalized Watershed Loading Functions). There are a total of 12 scenario outputs, including A2 and B2 scenarios with short, mid, and long terms of CGCM2 and HADCM3. The predicted annual inflows of short-term in CGCM2-A2 scenario are increase, while those in mid and long terms are decrease. The annual inflows of short and long terms in CGCM2-B2 scenario are also decrease. All scenarios from HADCM3 shows increase in inflows.
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Muchuru, Shepherd. "Predictability of seasonal rainfall and inflows for Water Resource Management at Lake Kariba." Thesis, 2015. http://hdl.handle.net/2263/44334.
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The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. The study characterized rainfall variability across the Lake Kariba catchment area, followed by describing prediction models for seasonal rainfall totals over the catchment and for inflows into Lake Kariba. The thesis therefore improved our understanding of rainfall variations over central southern Africa and provided evidence on how seasonal forecasts can be applied in order to potentially improve decision making in dam management.
The prediction of the seasons in which floods or droughts are most likely to occur involves studying the characteristics of rainfall and inflows within these extreme seasons. The study started off by analyzing monthly rainfall data through statistical analysis. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used low-level atmospheric circulation of a fully coupled ocean-atmosphere general circulation model over southern Africa, statistically downscaled to seasonal rainfall totals over the catchment. The verification of hindcasts showed that rainfall over the catchment is predictable at extended lead-times.
Seasonal climate forecasts need to be integrated into application models in order to help with decision-making processes. The use of hydro-meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. In order to reliably predict reservoir inflows for decision-making, the study investigated the use of a combination of physical and empirical models to predict seasonal inflows into the Lake. Two predictions systems were considered. First, antecedent seasonal rainfall totals over the upper Zambezi catchment were used as predictors in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method used predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model downscaled to the inflows. Inflow hindcasts performed best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season).Thesis (PhD)--University of Pretoria, 2015.
gm2015
Geography, Geoinformatics and Meteorology
PhD
Unrestricted
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Lima, Luana Medeiros Marangon. "Modeling and forecast of Brazilian reservoir inflows via dynamic linear models under climate change scenarios." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4687.
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The hydrothermal scheduling problem aims to determine an operation strategy that produces generation targets for each power plant at each stage of the planning horizon. This strategy aims to minimize the expected value of the operation cost over the planning horizon, composed of fuel costs to operate thermal plants plus penalties for failure in load supply.
The system state at each stage is highly dependent on the water inflow at each hydropower generator reservoir. This work focuses on developing a probabilistic model for the inflows that is suitable for a multistage stochastic algorithm that solves the hydrothermal scheduling problem.
The probabilistic model that governs the inflows is based on a dynamic linear model. Due to the cyclical behavior of the inflows, the model incorporates seasonal and regression components. We also incorporate climate variables such as precipitation, El Ni\~no, and other ocean indexes, as predictive variables when relevant.
The model is tested for the power generation system in Brazil with about 140 hydro plants, which are responsible for more than 80\% of the electricity generation in the country. At first, these plants are gathered by basin and classified into 15 groups. Each group has a different probabilistic model that describes its seasonality and specific characteristics.
The inflow forecast derived with the probabilistic model at each stage of the planning horizon is a continuous distribution, instead of a single point forecast. We describe an algorithm to form a finite scenario tree by sampling from the inflow forecasting distribution with interstage dependency, that is, the inflow realization at a specific stage depends on the inflow realization of previous stages.text
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Yu, Sin-Hong, and 余欣虹. "The Study for The Prediction of Inflows and Outflows of The Shihmen Reservoir by Using Artificial Neural Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/88713547262607534067.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
103
According to the literature, each Taiwanese receives only one-eighth of average rainfall per year in global. Esspaically the situation for Shihmen Reservoir which many compatriots depend on, that makes administration of water resource become more difficult and important. Due to the flow of reservoir is one of the important foundation data for decision management, the purpose of this study is to establish the prediction mode of flow to aid decision management. For administration, this study looks forward to reduce extra resources wasting of scheduling or purchasing water, and reduce disaster risk caused by misjudgment decision. For environment, this study expects stable flow be able to avoid high turbidity water and landslide caused by torrent. Shihmen Reservoir is research target in this study. The predictive variables of high explanatory power will be found though all possible regression procedure, then combine with Back Propagation Neural Networks and Time Delay Neural Networks respectively to construct forecasting modes. Then, to raise the accuracy and reliability of the prediction mode by combining with Time Delay Neural Networks and Nonlinear Auto-Regressive Moving Average control theory. At Final, evaluating accuracy of mode by error analysis and coefficient of efficiency. As a result, NARMA-L2 is the best mode. Back Propagation Neural Networks is better than other research the same method due to different input variables. Therefore, the study provides different methods and input variables for future research reference.
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Lin, Chih-Hung, and 林致弘. "Uncertainty analysis of reservoir inflow estimation-A case study in Shihmen reservoir." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ced5ak.
Full textAbstract:
碩士國立臺灣大學
生物環境系統工程學研究所
107
Due to the limitation of topography and climatic conditions, Taiwan has uneven spatial distribution during rainfall. In Taiwan, only about 20% of water can be used by people, so the distribution of water resources has become an important issue for the government. Due to the shortage of water resources, the reservoir is the most important water conservancy facility in Taiwan. If it can provide the simulated value of the future reservoir inflow, then the regulation of water resources will be of great help. Therefore, it is necessary to establish a reservoir inflow estimation model based on rainfall runoff model. Since the process of hydrological simulation is full of uncertainties, it is imperative to discuss reservoir inflows in a stochastic architecture. The research area of this paper is Shimen Reservoir, which is an important reservoir in northern Taiwan. It mainly provides agricultural water, public water, power generation and flood control, and is a multifunctional reservoir. From November of each year to May of the next year, it is the dry season of Taiwan, while the cultivation period of the first phase of rice cultivation is from January to July of each year. Due to the overlapping of the two times, the water situation in the first half of the year is often tight, so this study will target the dry season. The reservoir inflow is explored. In this study, the SWAT model (Soil and Water Assessment Tool) was used to estimate the inflow of Shimen Reservoir; the meteorological reproduction model was used to generate a single point of rainfall, and the Copula function was used to construct the multivariate distribution of the upstream rainfall station of Shimen Reservoir to simulate the upstream Rainfall distribution in the catchment area. The combination of the two can provide an uncertainty interval for reservoir inflow. Under this uncertainty structure, the reservoir inflow can be expressed as a probability density function. The probability density function of the inflow can provide a basis for government agencies to make decisions and prepare for the possible floods and droughts in the future. It is expected that this study will help Taiwan''s future water resources regulation.
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Hsu, Chih-Cheng, and 許志誠. "Seasonal Revising of Reservoir Inflow Grey Forecast Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/24701521881297092303.
Full textAbstract:
碩士中興大學
土木工程學系所
95
Taiwan is with dramatic variations both in spatial and temporal scale,80% rainfall concentration in wet season is June to October. The plentiful rainfall is in June of raining season and August of typhoon season. The rainfall diminution in dry season is November to next May. Thus, efficient water resources management becomes the main concern. The purpose of this thesis is to establish inflow forecast model from 10-day inflow of Shihman Reservoir and to improve the forecast model from hydrologic data deviation, extraordinary 10-day inflow, time lag of Grey forecast, and season factor. In this thesis research, the hydrologic data deviation is first rejected then the data is processed in logarithm and standardized. The new model with GE(1,1) and seasonal revising is used to analysis the reservoir inflow in each hydrologic year. The proof of the research is successful during the drought occurred in 2002 to 2003 especially with the normal process. This Thesis is presented as reference to an efficient hydrologic forecast.
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Jeng, Jia-Haur, and 鄭家豪. "Improved back-propagation networks for reservoir inflow forecasting." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/81929178641702467805.
Full textAbstract:
碩士國立臺灣大學
土木工程學研究所
96
The efficiency is an important issue for neural networks-based models, but the issue has received little attention in the hydrologic domain. Back-propagation networks (BPNs) are the most frequently used convectional neural networks (NNs). However, BPNs are trained by the error back-propagation algorithm which is a very time-consuming iterative process. To improve the efficiency, improved BPNs which are trained by a novel query learning approach are proposed. The proposed query learning approach is capable of selecting informative data from all training data. Then the improve BPNs can be efficiently trained with partial data. An application is conducted to demonstrate the superiority of the improved BPNs. Two kinds of BPN-based (the improved and the conventional BPN-based) reservoir inflow forecasting models are constructed and the comparison between the improved and the conventional BPN-based model is made. The results show that the performance of the improved BPN-based models is as good as that of the conventional BPN-based models, but the improved BPN-based models significantly required less training time than the conventional BPN-based models. As compared to the conventional BPN models, only about 50% of training time is required for the improved BPN-based models. The improved BPN-based models are recommended as an alternative to the existing models because of their efficiency.
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Chen, Chien-Hong, and 陳建宏. "The Influence of Rainfall Factor on Reservoir Inflow Forecasting." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/37061468465019297180.
Full text
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DING, CHONG-FENG, and 丁崇峰. "Study on the persistent phenomenon for reservoir inflow series." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/72293511218594402388.
Full text
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Shieh, H. J., and 謝宏智. "Inflow Prediction of Reservior System During Dry Season." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/15930640946996540250.
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"TROPHIC STATE AND FACTORS RELATING TO PHYTOPLANKTON COMMUNITY COMPOSITION AND DISTRIBUTION IN LAKE DIEFENBAKER, SASKATCHEWAN, CANADA." Thesis, 2015. http://hdl.handle.net/10388/ETD-2015-09-2258.
Full textAbstract:
Planktonic algae are useful as indicators of water quality because their composition and distribution reflects environmental condition in lakes. Therefore, understanding their dynamics can aid certain water quality management goals. Lake Diefenbaker is a large mesotrophic reservoir in the Canadian Prairies. Approximately 98 % of its inflow is from the South Saskatchewan River. The composition and ecology of the phytoplankton community has not been reported comprehensively since the 1980s. This is a potential problem for a reservoir with multiple end users. Therefore, I collected epilimnetic whole water samples along its length from June to October in 2011 and in 2012. I examined the phytoplankton community and related their distribution to environmental factors. A total of 72 phytoplankton genera were observed with the chlorophytes having the highest number of genera (33). The increased nutrient load and non-algal turbidity associated with high inflow from the South Saskatchewan River may be related to the dominance of the cryptophytes and bacillariophytes (together constituting ~89 % of the total phytoplankton biomass). The cryptophytes were abundant during periods of high flow rates and thermal stratification whereas the bacillariophytes were abundant during cool, isothermal conditions.
Lake Diefenbaker is characterized by numerous embayments. Some of these embayments are exposed to human activities including development (housing, golf courses, marinas) and livestock operations (e.g., cattle watering). These localized activities could increase the frequency or size of algal blooms that will adversely affect the water quality. Therefore, I compared the phytoplankton community composition from eight exposed embayments, four unexposed embayments and six main channel sites. Phytoplankton community compositions were not significantly different in exposed, unexposed embayments and main channel sites (P > 0.05). High flows may have overridden localized influence from embayments. Hence, similar environmental conditions were present in the embayments and main channel.
Blooms of cyanobacteria are of concern because of the potential of some genera to produce cyanotoxins. I examined cyanobacteria in Lake Diefenbaker. Cyanobacterial biomass was low in Lake Diefenbaker (< 5 %). However, I observed some potential toxin and bloom-forming genera that may threaten the water quality under different environmental conditions in the future.
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Liu, Min-wu, and 劉敏梧. "A Study on Forecast Model of Reservoir Inflow during Drought." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/72294904115153666482.
Full textAbstract:
碩士國立中興大學
土木工程學系
92
Taiwan is a rainfall abundant country, but the rainfall distribution is uneven both in time and space whenever and wherever drought may occur in ten years return period. Unexpectedly however, droughts came more frequently in recent two years, posing severe impacts on water resources utilization. Thus, beside water resources development, an effective way of water resources management becomes one of the major concerns in water resources utilization. In other words, the operation of reservoirs should be required more rational based crucially on the favorable hydrological conditions and right information. Therefore, to develop a reliable model as a tool for reservoir inflow forecast become necessary to water resources management. This paper, based on gray prediction system as a study model for developing the method of the reservoir inflow forecast, discusses the fundamentals of gray system theory, then the developing method of gray prediction model and possible limits in the model system so as to discuss the cause of problems we may encounter in the gray prediction, and try to find out the solutions and the model modification method. This study based on numerical analysis theory presents the terms of additional condition showing a new process of modeling method, and establish a modified grey model GMM(1,1). The modified model GMM(1,1) proved to be effective to improve the limits of the original model GM(1,1). Especially when extreme value comes across in numerical series, forecast errors found in three-term models like the original model GM(1,1) or the other modified models could be reduced to fit for the forecast in time series with larger dynamic characteristics. When applying this model to Shihmen Reservoir inflow forecast modification with seasonal variation correction factor or in this paper presented rainfall forecast correction factor, the GMM(1,1) can reduce the forecast errors. Particularly, the rainfall forecast modification method proves to be effective to increase higher prediction accuracy on condition that the rainfall forecast are compatible to the actual reservoir inflow. This paper is presented as reference to an effective way of hydrological prediction work.
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Chang, Chia-Chuang, and 張家銓. "Improved Self-organizing Linear Output Map for Reservoir Inflow Forecasting." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/25754392471904807349.
Full textAbstract:
碩士國立臺灣大學
土木工程學研究所
97
Based on self-organizing linear output map (SOLO), effective hourly reservoir inflow forecasting models are proposed. As compared with back-propagation neural network (BPNN) which is the most frequently used conventional neural network (NN), SOLO has four advantages: (1) SOLO has better generalization ability; (2) the architecture of the SOLO is simpler; (3) SOLO is trained much more rapidly, and (4) SOLO could provide features that facilitate insight into underlying processes. An application is conducted to clearly demonstrate these four advantages. The results indicate that the SOLO model is more well-performed and efficient than the existing BPN-based models. To further improve the peak inflow forecasting, SOLO with data preprocessing named ISOLO is also proposed. The comparison between SOLO and ISOLO confirms the significant improvement in peak inflow forecasting. The proposed model is recommended as an alternative to the existing models. The proposed modeling technique is also expected to be useful to support reservoir operation systems.
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Lin, Szu-ta, and 林思達. "Modification of the GWLF Model to Simulate the Feitsui Reservoir Inflow." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/5q6mx5.
Full textAbstract:
碩士國立中央大學
水文所
97
Although Taiwan has subtropical climate with annual rainfall of more than 2500 mm, limited water resource is still an issue due to short and steep rivers causing relative short residence time of surface runoffs. Reservoirs were often built to fulfill requirements of water resources and provide flood mitigation and power generation. The Feitsui Reservoir has been operated since 1987 and is the most important water resources for the 4 million people lived in the Taipei metropolitan. The original GWLF model was first applied to simulate the inflow of the Feitsui Reservoir with observed hydrological and meteorological data. Although monthly and annual inflows can be perfectly simulated, errors in daily inflows are significant. The main reasons are overestimate of evapotranspiration and lacking of temporal transmission in surface runoff. Computations of evapotranspiration are modified with effects of landuse, soil moisture resistance, and a threshold to hinder evapotranspiration during small rainfall days. Temporal transmission of surface runoff is modified with the ability of temporal routing by incorporating the concept of the Maskingum method. The new model has a better capability to simulate daily inflows of the Feitsui reservoir than the original GWLF model. By examining the observed and predicted daily inflows with both models, the correlation coefficient is 0.871 (original is 0.744), the coefficient of efficiency is 0.843 (original is 0.724), the root mean square error is 0.718 cm (original is 0.902 cm). For extreme rainfall events, the new model overcomes the problems of early peak flows, overestimated peak, and steep recession as commonly appeared in the original model. The new GWLF model now can be more appropriate to simulate daily, monthly, and annual inflows of the Feitsui Reservoir.
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Kung, Wen-Jui, and 龔文瑞. "Assessment of Inflow for the Transbasin Diversion Tunnel of Tsengwen Reservoir." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/65754754189729258455.
Full textAbstract:
碩士國立成功大學
資源工程學系碩博士班
93
A computer program, MODFLOW-SURFACT, is applied to simulate the inflow of the east part of transbasin diversion tunnel of Tesengwen Reservoir during tunnel excavation. The tunnel is located in the complex geological structures with deep depth cover rocks and water pressure. The field data is investigated and collected in order to establish two-dimension and three-dimension hydrogeology model and to estimate the inflow under the several specific conditions, for instance, single tunnel advance rate, steady and accumulate condition etc. The simulational results showed the maximum inflow during each tunnel advance is the value of 0.695 l/ s•m occurred in Kaochung fault. The intermediate inflow is the value in the range between 0.55 l/ s•m to 0.547 l/ s•m occurred in the tunnel cross-section through two faults, Laonung fault and Kaochung fault. On the other hand, the steady inflow under steady analysis is the value of 0.43 l/ s•m in the east part of the Laorenxi anticline. Furthermore, the steady inflows are the values in the range between 0.2 l/ s•m to 0.3 l/ s•m for the Kaochung fault, Laonung fault, the sandstone between Laonung fault and Kaochung fault and the shale proximity the west entrance. The impact of tunnel excavation on groundwater recharge of water resource environment seems to be less significant because the accumulated inflow of the study area during each tunnel advance is the value of 2849.9 tons/year which is significantly less than the value of 750.6 megaton/year from groundwater recharge of the water basin.
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Chen, Chien-hung, and 陳建宏. "Pollutant load estimation of reservoir inflow during the period of storms." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/74975774382698111752.
Full textAbstract:
碩士國立臺灣大學
土木工程學系研究所
86
Reservoir eutrophication in Taiwan is getting severe, due to the overdevelopm ent of catchment. Water quality data in Taiwan is evidently absent during peri ods of flooding and high flows. For the possible controlof water quality, an e fficient method of pollutant load estimation is needed to make water resource conservation and enduring use become possible.The study of pollutant load est imation was concentrated on the absorbable material, such as total phosphorus and toxic material. The charactericial of absorbability was used to develop th e estimation equation. The estimation equationwas applied to derive the equati on of total phosphorus load of reservoir inflow.The research applied Chiu''s v elocity distribution and sediment distributionequations. This study also made use of the efficient methods of discharge and sediment estimation developed by C.-L. Chiu. In additional, the water quality concentration distribution on t he vertical was simulated by binomial to estimate the total phosphorus load of reservoir inflow. The data of Song-mou Station was collected to verify the d erived equation.
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Huang, Yi-Chi, and 黃怡綺. "Study of Applying Time Series Analysis methods on Simulating of Inflow of Reservoir—WuSeh Reservoir as an Example." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/73634521229065183225.
Full textAbstract:
碩士國立中興大學
水土保持學系所
95
The supply and demand of water resource is imbalance in Taiwan, if the reservoir management agency can predict accurately the inflow of reservoir from its up stream river, it should be helpfull for regulation and control of the reservoir. This research collects the historical daily and 10-day inflow of reservoir watersheds, and uses time series analysis methods, its theory is already to be developed completely, to analyse its periodicity and secular trend separately, by way of autoregressive integrated moving average (ARIMA) models infer optimization parameter, for simulating and predicting the inflow of reservoir watersheds. On the other hand, the best way in which two series obtain assays and proves, compare the well and poor situated results with the observed inflow data, use to help the efficient operation of the reservoir water resources. This research uses the inflow data of Wu-Seh (2) hydrologic station of Wu-seh reservoir of Jhuo-Shuei river basin science January of 1985 till December of 2005. The analyzed step was as following: A. Make the daily and 10-day inflow series in order and file, then examine whether it is belong to white noise or not. B. Carry on the daily and 10-day inflow series to be normalized and differential operated separately, then, draw the seasonality, circulation cycle and secular trend of inflow. C. Use the daily and 10-day inflow series science 1985 till 2004, finally to set up the ARIMA(p, i, q)×(P, I, Q)k model. D. Lastly, I tried to predict the inflow data of the 21-th year with both the erected two model and then compared it with the observed inflow data in 2005 year, by way of the best two ARIMA model. Three conclusiones were found as following: 1. To adopt MA model for simulation and prediction of the daily inflow series is not so well as expectancy for this series’s randomness is so high that both of its autocorrelations and partial autocorrelations can not convergence. 2. Relatively, because the 10-day inflow series shows as characteristics of seasonality, periodicity and randomness, that means the factors are complicated, and will influence the series apparently, so to adopt the multiply, high rank seasonal ARIMA model for simulating and predicting the 10-day inflow series, it is found that the prediction ability of the best model is very well than the daily inflow series. 3. This study used 10-day as the time unit to predict the inflow series. Not only it can obey the norm curve but also can afford a concrete and practical analysis tool, ARIMA, and then can predict correctly an inflow from the upstream creek to the reservoir. It can be an authority for supporting the reservoir agency to adjust and control the water resources.
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Zarea, Marwan Annas H. "A Comprehensive Evaluation of Reservoir Inflow and Wellbore Behavior in Intelligent Wells." Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8367.
Full textAbstract:
Intelligent well technology is a relatively new technology that has been adopted
by many operators in recent years to improve oil and gas recovery. Because of its
complexity, accurate modeling of the reservoir and wellbore performance in the
multilateral well application is critical to optimize well production. Little work has been
performed on understanding the flow behavior through the main component of the
intelligent well, the inflow control valve. This study presents a comprehensive model to
quantify the reservoir and well performance in the horizontal laterals of the intelligent
multilateral well. Moreover, it combines this model with equations to evaluate the flow
rate and pressure profile through the inflow control valves. As a result of this study, the
well performance of intelligent wells can be predicted and optimized.
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Lo, Ying-Chin, and 羅英秦. "Multi-step-ahead reservoir inflow forecasts using neural networks with ensemble method." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/93972619180271311873.
Full textAbstract:
碩士國立臺灣大學
生物環境系統工程學研究所
101
Due to unique geographical location of Taiwan, an average of 3.5 typhoons attack Taiwan each year. In addition, the particular topographical terrains of Taiwan make rivers short and steep such that rivers rapidly flow from catchments to reservoirs within a few hours during typhoon events. It will be very helpful and useful for reservoir operation management if reservoir inflow information can be provided in the next few hours after typhoons initially arrive. This study investigates the rainfall-runoff process of reservoir catchment by using two different rainfall information: rain gauge precipitation data and radar rainfall data (QPESUMS: Quantitative Precipitation Estimation and Segregation Using Multiple Sensors). The Zhengwen Reservoir catchment is the study area. The correlation analysis results show that it takes only two hours for rainfall to travel from catchment to reservoir in the study area. Therefore, this study aims to build up multi-step-ahead reservoir inflow forecast models through artificial neural networks based on QPESUMS and inflow information. The results indicate that all the BPNN, ANFIS and RNN models have excellent estimation performance. The BPNN model performs the best for one- to three-hour-ahead forecasts, while the RNN model has the best performance for four- to six-hour-ahead forecasts. The ANFIS model is superior to the other models for peak flow forecasts. The results demonstrate that each neural network has its own distinct advantages from others. Ensemble forecasting was originated from Atmospheric sciences and has been developed for years. In this study, we build up an ensemble forecast model by incorporating the outputs of three constructed forecast models into the BPNN to produce multi-step-ahead reservoir inflow forecasts, and further conduct the sensitivity analysis to summarize the weights of individual models incorporated in the ensemble forecast model for each time step. The results demonstrate that the ensemble forecast model can provide more reliable and accurate multi-step-ahead reservoir inflow forecasts than individual models incorporated.
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Hsu, Yu-Lung, and 許育榮. "Prediction and Supplement of Hu-Tou Pei Reservoir Inflow using Precipitation Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/93791325848219431332.
Full textAbstract:
碩士國立屏東科技大學
土木工程系所
98
Hu-Tou-Pei reservoir, located in Tainan County, Taiwan, is managed by the Chia-Nan Irrigation Association. The reservoir is of medium scale and the catchment area is 715 ha. Unfortunately, there is no precipitation station and the water intake monitoring records are from the upstream reservoir catchment area. The relationship between the amount of reservoir water intake and the rainfall in the catchment area is determined using rainfall records from the spillway; the spillway discharge is used to calculate the water intake. In this study, the reservoir was regarded as a system in which a precipitation - runoff model was considered as a black box model. The precipitation was used as the input variable and the water intake of the reservoir was used as the output variable. The hourly rainfall data from 13 June, 2007 to September, 2008 were used to establish the relationship between the rainfall amount and the reservoir water intake using a multiple regression method intended to establish the reservoir water intake forecast model. Only a few measurements were taken at first. The regressed model is as follows:Qt=1071 Pt + 1442 pt-1+ 942 pt-2 + 9235, in which is reservoir water intake; is amount of precipitation at time t; and were rainfall amounts at one and two hours before time t. This study used the gray forecast model to estimate the amount of rainfall. The results showed that the approaches were applicable to a reservoir having a small catchment area. The method of missing data addendum is in light of reference the reservoir operation.
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Li, Yen-Chuan, and 李晏全. "Monthly and Seasonal Inflow Forecasting of Shihmen Reservoir during the Dry Seasons." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37353739790513337359.
Full textAbstract:
碩士國立成功大學
水利及海洋工程學系碩博士班
94
If the reservoir inflows can be forecasted precisely beforehand, they may benefit the reservoir operation and management in Taiwan. The long-term inflow forecasting system of reservoir combines a continuous rainfall-runoff model with the long-term weather outlook provided by the Central Weather Bureau to forecast one-month and three -month ahead inflows with the ten-day and one-month time steps. There are several tasks in the present study, including (1) the developing of a rainfall-runoff model based on the daily time step, (2) the developing of ten-day and monthly model and further comparing the accuracy among ten-day, monthly and daily model, (3) the combining of the modified long-term weather outlook and the continuous rainfall-runoff with different time steps to forecast the monthly and three-month inflows. The results reveal the continuous rainfall-runoff model has good performances on daily, ten-day and monthly flow simulation. The comparison shows that the daily time scale model has better performances than the ten-day and monthly one. Forecasted inflows by using different time scale model are compared with the historical average inflows. The comparison indicates that the proposed inflow forecasting system has better results in inflow forecasting. In conclusion, the daily rainfall-runoff model can get the better accuracy. Daily time scale can be easily used in different time scale forecasting data of Central Weather Bureau in the future. The inflow forecasting may support the reservoir management for operation decision and drought warning.
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Kuo, Hsiu-yun, and 郭秀筠. "Studies on Reservoir Inflow Estimating Model by Using the Radar-based Quantitative Precipitation." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/59711235990950670536.
Full textAbstract:
碩士淡江大學
水資源及環境工程學系碩士班
103
In recent years, the typhoons and extreme rainfall days increases, the precipitation is unsteady in different places and during different seasons, and it usually concentrates in moist season. The typhoon causes results in severe flood inundation. During the typhoon hit, using the rainfall intensity data of typhoon flood events, to quickly grasp the reservoir inflow, it is expected that results of this study could be used for online reservoir operation in the future. In this study, we focus on the Shihmen reservoir upstream catchment area, using the relationship of rainfall factor and reservoir inflow to separately establish of cumulative inflow before peak flow, cumulative inflow after peak flow, total cumulative inflow and peak inflow estimating models. The study use the stepwise regression analysis to built model, with 95% confidence interval and cross-validation test the model, and finally using the radar-based quantitative precipitation estimation data verify the model. The results shows that using the stepwise regression analysis to establish reservoir inflow estimating model, the t-test are less than 0.05. The study found that four kinds of inflow are related to the total cumulative rainfall factor, and adding the average rainfall intensity factor, its coefficient of determination R2 value of 0.85 or more are up to. In this study, the cross-validation results showed that only three events are extreme value, the other 30 events of results are satisfactory. In this study, we use the radar-based quantitative precipitation estimation data in 4 reservoir inflow estimating models, the results shows that the peak inflow validation results is the four kinds of best estimate type.
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Chou, Yang-Ching, and 周揚敬. "Using support vector machines to improve reservoir inflow forecasting during typhoon-warning periods." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44933898752793153567.
Full textAbstract:
碩士國立臺灣大學
土木工程學研究所
96
In this paper, effective reservoir inflow forecasting models based on the support vector machine (SVM), which is a novel kind of neural networks (NNs), are proposed. Based on statistical learning theory, the SVMs have three advantages over back-propagation netwoks (BPNs), which are the most frequently used convectional NNs. Firstly, SVMs have better generalization ability. Secondly, the architectures and the weights of the SVMs are guaranteed to be unique and globally optimal. Finally, SVM is trained much more rapidly. An application is conducted to clearly demonstrate these three advantages. The results indicate that the proposed SVM-based models are more well-performed, robust and efficient than the existing BPN-based models. In addition to using SVMs instead of BPNs, typhoon characteristics, which are seldom regarded as key input for inflow forecasting, are added to the proposed models to further improve the long lead-time forecasting during typhoon-warning periods. A comparison between models with and without typhoon characteristics is also presented to confirm that the addition of typhoon characteristics significantly improves the forecasting performance for long lead-time forecasting. In conclusion, the typhoon characteristics should be used as input to the reservoir inflow forecasting. The proposed SVM-based models are recommended as an alternative to the existing models because of their accuracy, robustness and efficiency. The proposed modeling technique is expected to be useful to improve the reservoir inflow forecasting.
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Wu, Chi-Wei, and 伍啟維. "Investigation of the Reservoirs Inflow Sediment and Water Quality for Sustainable Use." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/60742606558492475267.
Full textAbstract:
碩士國立中興大學
水土保持學系
88
The purpose of this research is to estimate reservoir inflow sediment yield and water quality. Applying the conception of sustainable life, the rational interval of the inflow sediment can be anticipated. The result should be useful in sediment control of reservoirs. Sediment yield model in reservoir watershed is based on the soil erosion index model. Water quality model contains the estimation of the phosphorus loading and the phosphorus mass balance model, which can analyze the effect of inflow sediment raise eutrophication. Considering the relation among the total phosphorus PI (ton /yr), the inflow water Q (10^6m3/yr), and the soil erosion quantity SE (ton/ha-yr), the equation of Der-Ji reservoir can be expressed by the following equation: PI = 2.23*10^-2Q + 4.7*10^-4SE*A*SDR. Where A represents the area of watershed (ha) and SDR is the sediment delivery ratio (%); 2.23*10^-2 implies the dissolved phosphorus concentration (mg/l) and 4.7*10^-4 is the ratio of dissolved phosphorus and suspended solids. In the case of Der-Ji reservoir, the rational interval of the inflow sediment yield is bounded from 2.7 to 10.4 ×10^4 m3/yr. Considering the water quality, the maximum will be reduced to 4.5×10^4 m3/yr. By the present situation without dredging, Der-Ji reservoir can maintain the water resources sustainable only if the annual sediment yield is lower than 7.6×10^4 m3.
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Wu, Lei-Ken, and 吳雷根. "A study on Long-term Inflow Forecasting of Tsengwen Reservoir during the Dry Seasons." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/qaevh8.
Full textAbstract:
碩士國立成功大學
水利及海洋工程學系碩博士班
92
Long-term inflows of reservoir form upstream catchment are important information for reservoir operation. If the inflows of reservoir can be forecasted precisely beforehand, that may benefit the reservoir operation and management. Therefore, we attempt to develop a long-term inflow forecasting system of reservoir during the dry seasons and apply the system in the upstream catchment of Tsengwen reservoir, which is to support the reservoir management for operation decision and drought warning. The long-term inflow forecasting system of reservoir that combines a continuous rainfall-runoff model with the long-term weather outlook provided by the Central Weather Bureau to forecast three ten-day ahead inflows. There are several tasks in the present study, including (1) developing a rainfall-runoff model based on ten-day time scale in order to match up the time scale of reservoir operation, (2) proposing a transforming method to correct the long-term weather outlook for the study area, (3) forecasting one to three ten-day ahead inflows of reservoir, and (4) developing a window-based long-term inflow forecasting system of reservoir to provide users with convenient operation. Model calibration and verification were performed from 28-year historical records, and the results reveal the continuous rainfall-runoff model has good performances on ten-day flow simulation. One to three ten-day ahead inflows forecasted in the study were compared with the historical average ten-day inflows, which are always chosen as a reference for reservoir classical operation. The comparison indicates that the proposed inflow forecasting system has better results for one to three ten-day inflow forecasting. Finally, we use the Visual-Basic 6.0 software coupled with Fortran software to develop a window-based long-term inflow forecasting system of reservoir.
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Nass, Maria A. "Inflow Performance Relationships (IPR) for Solution Gas Drive Reservoirs -- a Semi-Analytical Approach." 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8028.
Full textAbstract:
This work provides a semi-analytical development of the pressure-mobility behavior of solution gas-drive reservoir systems producing below the bubble point pressure. Our primary result is the "characteristic" relation which relates normalized (or dimensionless) pressure and mobility functions. This formulation is proven with an exhaustive numerical simulation study consisting of over 900 different cases. We considered 9 different pressure-volume-temperature (PVT) sets, and 13 different relative permeability cases in the simulation study. We also utilized 7 different depletion scenarios.
The secondary purpose of this work was to develop a correlation of the "characteristic parameter" as a function of rock and fluid properties evaluated at initial reservoir conditions such as: API density, GOR, formation volume factor, viscosity, reservoir pressure, reservoir temperature, oil saturation, relative permeability end points, corey exponents and oil mobility:
We did successfully correlate the characteristic parameter as a function of these variables, which proves that we can uniquely represent the pressure-mobility path during depletion with specific reservoir and fluid property variables, taken as constant values for a particular case.
The functional form of our correlation along with all relevant equations are shown on the body of this document.
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Cheng, Guo-rong, and 程國榮. "Applications of Monthly and Annual Water Balance Models for Estimating Inflows of the Shihmen and Feittsui Reservoirs." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/41801264052156714202.
Full textAbstract:
碩士國立中央大學
水文與海洋科學研究所
100
Both the Shihmen and Feitsui Reservoirs are the most important reservoirs in northern Taiwan providing water resources for domestic, industrial and agricultural usages. Booming social and economic developments in recent years requires increasing demands of water resources. On the other hand, climate variability brought challenges on reservoir managements. Predicting long-term variability and characteristics of reservoir inflows are essential to water resources planning and managements, which are not necessary to be estimated by distributed hydrological models. Monthly and Annual hydrological models provide alternative approach with simplicity to understand long-term inflow variations of watershed to support water resources planning and management. In this study, we applied monthly and annual water balance model to estimate inflows of the Shihmen and Feitsui Reservoirs. For annual scales, the water balance equation was given based on the Budyko assumptions by neglecting changes of subsurface storages. For monthly scales, the Thornthwaite - Mather Water Balance (TMWB) model was employed to estimate reservoir inflows. In this study, we collect the Shihmen and Feitsui Reservoir inflow and meteorological data for model calibrations. Result showed that the combination of Hamon equation (for estimating potential evapotranspiration) and Fu equation (for estimating evapotranspiration) provided the best skill on estimating annual reservoir inflows. The TWMB has skills on estimating monthly reservoir inflows. However, both monthly and annual water balance approaches underestimated inflows of extreme events. Overestimations of potential evapotranspiration were suggested to be the cause of less skill on extreme events. A reduction coefficient was proposed to modify the amount of potential evapotranspiration estimated in both monthly and annual water balance models. For the TMWB model, a layer of subsurface storage was added with a base flow coefficient. Results showed modifications proposed slightly improve the skills of both approaches on estimating reservoir inflows. In order to ease complicated calibration processes, a Graphical User Interface of models employed in this study was developed via the Microsoft Visual Basic 2012 RC to provide a user friendly interface with simultaneous and instantaneous graph drawing capability.
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Cheng, Chun-Teng, and 鄭竣騰. "A Study of Flood and Drought Characteristic Using HHT─A Case Study of Shihmen Reservoir Inflow." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/sbsw9b.
Full textAbstract:
碩士淡江大學
水資源及環境工程學系碩士班
101
The influence of the extreme climate on rainfall and flow in time and space distribution are becoming less equal, wet period whether there is an enhanced flow of the possibility of flooding if there is increasing trend year by year; another phenomenon is more frequent droughts, continuous drought whether the time getting longer and longer. In order to understand the extreme weather in Taiwan why the characteristics of flood and drought, in this study, Shihmen Reservoir inflow into annual maximum daily flow, total flow in the wet season, the annual number of days and the longest droughts in the dry season the total flow four kinds of cases, respectively, Hilbert - Huang Transform (HHT) and Fourier transform for generating a time-frequency analysis and time-frequency diagram for comparison. Among the Fourier transform of the time-frequency diagram showing the energy is not enough focus, frequency distribution broader, less accurate, can not determine the exact local time points changes in the frequency and energy, however, faithfully rendering HHT frequency variation between the junction, the energy is more concentrated, higher resolution available locally exhibit the relationship between time and frequency, so choose HHT analysis with test of hypothesis. Cases using the moving average method from the time grouped, each with a set of time as the reference time of the energy group time and energy of the other test, the time of the test in each group the respective accumulated and analyzed by observing the number of flows in recent years emerging trends, period, whether to render exacerbate or mitigate the phenomenon, and then explore the characteristics of flood and drought. From the annual maximum daily flow rate and total flow in the wet season results, floods and wet season total flow individually increasing trend; longest drought in the number of days from the year and the annual dry season, the total flow results, nearly 15 years, droughts have increased year by year the number of days trend, while the total flow during the dry season, did not show a declining trend. Based on the above point of view, the coexistence of droughts and waterlogging with extreme weather phenomena remarkable every year.
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Kuo, Sui-An, and 郭隨安. "Long lead-time reservoir inflow forecasting by adapting a rainfall-runoff model with ensemble precipitation forecasts." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j78qp6.
Full textAbstract:
碩士國立臺灣大學
土木工程學研究所
106
Taiwan is located on the main track of western Pacific typhoons, and approximately three to four typhoons hit Taiwan per year. Typhoons accompanied by heavy rainfall often results in a huge amount of runoff, which causes downstream floods and induces great disasters. Meanwhile, the reservoir operator should assess flood control operations carefully. The dam security and downstream residents are both taken into consideration. In this case, prerelease for real time flood operation is important. Accurate reservoir inflow forecasting with enough lead time helps the reservoir operator to make operational policies in advance during typhoons. For the aforementioned reasons, a new long lead-time reservoir inflow forecasting approach by means of forcing the rainfall-runoff model with ensemble precipitation forecasts is proposed to yield 1- to 72-h ahead reservoir inflow forecasts of the Shihmen reservoir during typhoons. The structure of this study is composed of three parts: First, a novel rainfall-runoff model which combines the HEC-HMS model with support vector machine is proposed. The proposed rainfall-runoff model is calibrated and validated with twelve typhoons during 2008 to 2011. Second, with ensemble quantitative precipitation forecasts from Taiwan Typhoon and Flood Research Institute being the meteorological forcing, the proposed rainfall-runoff model provides ensemble reservoir inflow forecasts for seven typhoons from 2012 to 2015. Third, the study integrates ensemble reservoir inflow forecasts using random forest. Taking Typhoon Soudelor in 2015 for example, results show that coupling HEC-HMS with SVM provides more reasonable ensemble distribution than using only HEC-HMS. Compare with the ensemble mean, reservoir inflow forecasts from random forest have less uncertainty and the advantage of extra lead time, particularly 48 hours to 72 hours. The proposed model could provide accurate 3-day reservoir inflow forecasts immediately after the Typhoon Soudelor warning was issued. The error of cumulative inflow was only 4.03%. According to the proposed approach, the authority may efficiently operate the reservoir and balance a trade-off between ‘gaining more flood buffer for dam security paying the expense of increasing shortage risk’ and ‘ensuring adequate water resources by enduring the potential of flooding damage’.
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Tang, Yung-Hsiang, and 唐雍翔. "The Effects of Weather and Inflow Water Temperature on the Eutrophic Condition of Shin-Shan Reservoir." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47131027852451185863.
Full textAbstract:
碩士國立臺灣大學
環境工程學研究所
103
Shin-Shan Reservoir located at Anle Dist., Keelung City in Taiwan is an off-channel reservoir, providing water for people living in Keelung City and surrounding area of New Taipei City. In recent years, Shin-Shan Reservoir had heavy algal blooms in summer when the water body was stratified. In this study, we adopted CE-QUAL-W2 model and integrated the buoyant jet mechanism into it to simulate the water quality of Shin-Shan Reservoir. The relationships between inflow rate, temperature and the degree of eutrophication were investigated, as well as those between the variation of eutrophication and the characteristics of inflow. The model was calibrated and verified with the field data in 2014 and 2010, respectively. The results show that the predicted concentrations of phosphorus, nitrate, ammonium and chlorophyll a are close to real data. However, the total phosphorus concentration was underestimated by the model. Model simulations reveal that temperature of inflow water affects the final position of inflow which becomes a buoyant jet in a stratified water body. When the inflow jet is dispersed near the water surface, the nutrients will be loaded in the epilimnion or metalimnion, leading to severe eutrophication. CE-QUAL-W2 model with the buoyant jet mechanism is able to simulate the water quality of Shin-Shan Reservoir better than the model without consideration of the behavior of the buoyant plume. Furthermore, river temperature affects the final position of inflow. If the river water is cooler than the water of the thermocline, the nutrients will not be loaded into surface water, and there will be less growth of algae. On the contrary, if it is warmer, the algae will bloom heavily. Therefore, the authority of Shin-Shan Reservoir should closely watch the temperature of river water when it is climbing up quicker than that of the water in the reservoir, and choose appropriate operational approach to preserve the water quality in the reservoir.
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Hsu, Tseng-Fa, and 許增發. "Ecological Impact of Check Dams on Fish Distribution in the Major Inflow of Tseng-Wen Reservoir." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/zu4fk5.
Full textAbstract:
碩士國立成功大學
水利及海洋工程學系專班
92
The Tsengwen Reservoir, located in the Southern Taiwan, is a multi-objectives utility Reservoir. The upstream rivers of its watershed have caused severe sediment disasters. Over the years, one of the basic prevention methods has been to build many check dams along rivers. In the early days, people raised the height of the dam to increase the space for soil storage. To protect the dam embankment in upstream areas and improve the stability of all tributaries, the water resources management authority planned and built a series of check dams along the major rivers to provide water to the Tsengwen Reservoir. However, the negative impacts of check dams upon fish migration have been brought to the public’s attention and severely criticize. The purpose of this study is to identify the impact of serial check dams on fish communities. We used the similarity coefficients and diversity index of six main check dams on the vast data collected to identify the effectiveness of chemical habitat conditions, physical habitat conditions, fish species, and characteristics of check dams. The result of our study showed that the chemical habitat conditions have no apparent spatial differences. Similar fish communities are aggregated in groups according to the spatial locations of the serial check dam. We expect this exercise to shed light on the spatial differences among fish communities in the watershed area of the Tsengwen Reservoir.
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Wu, Sheng-Hui, and 吳聲暉. "The Study on the Relation between Seepage of the Shiue-Shun Tunnel and the Inflow of the Fei-Tsuei Reservoir." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/32495856102321700481.
Full textAbstract:
碩士國立成功大學
資源工程學系碩博士班
93
Abstract Fei-Tsuei Reservoir is the main source of water supply in Taipei. The purposes of this study were to investigate the impact of the excavation of Shiue-Shun tunnel on water supply. The base-flow record method was used to estimate groundwater recharge and to separate base-flow from daily river inflow at the watershed of Fei-Tsuei Reservoir. The diversity of water amount due to the change of monthly base-flow and the excavation of Shiue-Shun tunnel was also discussed. The 2-D and 3-D hydrogeological models were established for the watershed of Fei-Tsui Reservoir. The numerical solutions with MODFLOW and analytical solutions were used to estimate the seepage during tunnel excavation and then compare it with field measurement. The model calibration was also performed in terms of groundwater level when the east and west entrances of Shiue-Shun tunnel were excavated. The results showed that the estimate of the groundwater recharge of Fei-Tsui Reservoir was 0.39 billion tons per year and 0.3 billion tons per year for the base-flow record method and the low-flow analysis, respectively. The filed investigated result showed that the seepage of the excavation of Shiue-Shun tunnel was about from 3.75 to 5.09 million tons per year. It indicated that the seepage of the excavation tunnel is only in the ratio range between 1.25% to 1.70% of groundwater recharge. On the other hand, according to the relationship between base-flow variation per month and the excavation change during 1996 to 1998, especially during 1996/03~1996/07, 1997/05~1997/10 and 1998/03~1998/12, it showed that the positive correlation is provided in terms of the relation between the seepage of the tunnel and the base-flow of the watershed of the Fei-Tsui reservoir. It could imply that there exit the connected channels between the watershed and tunnel. The results of the analytical model showed that the tunnel inflow amount at TBM 10th besieged place (mileage is 39K＋074) was in the range between 163 to 168(L/sec) with which was corresponded to the real field amount between 150 and 180(L/sec). On the other hand, results of 2-D and 3-D numerical model were provided that the inflow were 12.00 and 1.68(L/sec) respectively. It indicated that the inflow from 2-D and 3-D numerical model were difference from real field amount. The 3-D model was also used to simulate the impact of the inflow of tunneling excavation at eastern and western part on ground water recharge on the watershed of Fei-Tsui Reservoir. The results show that the excavated inflow of eastern part was larger than that of western part. The effect of the excavated inflow of eastern part on ground water recharge on Tou-Cheng Watershed was quietly less significant. When the tunnel was excavated after 90 days, only 1% variation and 3.37% variation of groundwater level drawdown of groundwater level at the eastern part and in the western part respectively within the range of the radius 3 kilometers. In conclusion, the effect of excavation advance of Shiue-Shun tunnel on the change of groundwater level of watershed of Fei-Tsui Reservoir is less significant.
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Yao, Chung-Kai, and 姚重愷. "Simulation and Analyses of the Effects of Hypolimnetic Aeration and Inflow Water Temperature on the Eutrophic Condition of Shin-Shan Reservoir by Using CE-QUAL-W2 Water Quality Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/98418784514003597230.
Full textAbstract:
碩士國立臺灣大學
環境工程學研究所
101
During the past few years, Shin-Shan Reservoir, an off-channel reservoir and major water source for Keelung city and a part of Taipei metropolitan area, became seriously eutrophic due to elevated pollutant loading and possibly more frequent extreme-climate events. In this study, CE-QUAL-W2 water quality model was used to evaluate the effects of hypolimnetic aeration on water quality of Shin-Shan reservoir and to investigate the impacts of the higher temperature of inflow water than in reservoir during unusual climate events on the water quality. The model was calibrated and verified with the measured data in 2010 and 2011, respectively. The results of simulation show good fit to the hydrodynamic phenomena such as water levels and temperatures. However, the simulated results underestimate the total phosphorus concentration and concentration of chlorophyll a during the summer in 2011. Overall, the calibrated model provides a satisfactory fit of simulated results to the actual data in this reservoir. Simulations indicated a significant lower phosphorus concentration under the scenario of hypolimnetic aeration. The concentration of nitrate in the water body would increase slightly after a short period of hypolimnetic aeration due to the oxidation of ammonia to become nitrate, but would reach a steady state once most of ammonia had been transformed. The concentration of chlorophyll a would not change significantly with the hypolimnetic aeration in the current year, presumably due to less direct impact of hypolimnetic aeration on the surface water quality. Nevertheless, the concentration of chlorophyll a had an obvious decrease in the next year, due to the lower phosphorus flux released from sediments after hypolimnetic aeration in the previous year. In conclusion, the simulation analyses show that for a higher temperature of inflowing water than the temperature of epilimnion, the concentration of chlorophyll a would be increased in Shin-Shan Reservoir, which would have great impacts on the eutrophic state of Shin-Shan Reservoir.