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Author: Matthew James Fleming
Publisher:
ISBN:
Category : Dale Hollow Lake (Tenn. and Ky.)
Languages : en
Pages : 186
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Author: Matthew James Fleming
Publisher:
ISBN:
Category : Dale Hollow Lake (Tenn. and Ky.)
Languages : en
Pages : 186
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Book Description
Author: Michael B. Abbott
Publisher: Springer Science & Business Media
ISBN: 9400902573
Category : Science
Languages : en
Pages : 323
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Book Description
It is the task of the engineer, as of any other professional person, to do everything that is reasonably possible to analyse the difficulties with which his or her client is confronted, and on this basis to design solutions and implement these in practice. The distributed hydrological model is, correspondingly, the means for doing everything that is reasonably possible - of mobilising as much data and testing it with as much knowledge as is economically feasible - for the purpose of analysing problems and of designing and implementing remedial measures in the case of difficulties arising within the hydrological cycle. Thus the aim of distributed hydrologic modelling is to make the fullest use of cartographic data, of geological data, of satellite data, of stream discharge measurements, of borehole data, of observations of crops and other vegetation, of historical records of floods and droughts, and indeed of everything else that has ever been recorded or remembered, and then to apply to this everything that is known about meteorology, plant physiology, soil physics, hydrogeology, sediment transport and everything else that is relevant within this context. Of course, no matter how much data we have and no matter how much we know, it will never be enough to treat some problems and some situations, but still we can aim in this way to do the best that we possibly can.
Author: Bellie Sivakumar
Publisher: World Scientific
ISBN: 9814464759
Category : Science
Languages : en
Pages : 542
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Book Description
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Author: Qingyun Duan
Publisher: John Wiley & Sons
ISBN: 087590355X
Category : Science
Languages : en
Pages : 356
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Book Description
Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.
Author: Hongli Liu
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 205
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In hydrologic modeling and forecasting applications, many steps are needed. The steps that are relevant to this thesis include watershed discretization, model calibration, and data assimilation. Watershed discretization separates a watershed into homogeneous computational units for depiction in a distributed hydrologic model. Objective identification of an appropriate discretization scheme remains challenging in part because of the lack of quantitative measures for assessing discretization quality, particularly prior to simulation. To solve this problem, this thesis contributes to develop an a priori discretization error metrics that can quantify the information loss induced by watershed discretization without running a hydrologic model. Informed by the error metrics, a two-step discretization decision-making approach is proposed with the advantages of reducing extreme errors and meeting user-specified discretization error targets. In hydrologic model calibration, several uncertainty-based calibration frameworks have been developed to explicitly consider different hydrologic modeling errors, such as parameter errors, forcing and response data errors, and model structure errors. This thesis focuses on climate and flow data errors. The common way of handling climate and flow data uncertainty in the existing calibration studies is perturbing observations with assumed statistical error models (e.g., addictive or multiplicative Gaussian error model) and incorporating them into parameter estimation by integration or repetition with multiple climate and (or) flow realizations. Given the existence of advanced climate and flow data uncertainty estimation methods, this thesis proposes replacing assumed statistical error models with physically-based (and more realistic and convenient) climate and flow ensembles. Accordingly, this thesis contributes developing a climate-flow ensemble based hydrologic model calibration framework. The framework is developed through two stages. The first stage only considers climate data uncertainty, leading to the climate ensemble based hydrologic calibration framework. The framework is parsimonious and can utilize any sources of historical climate ensembles. This thesis demonstrates the method of using the Gridded Ensemble Precipitation and Temperature Estimates dataset (Newman et al., 2015), referred to as N15 here, to derive precipitation and temperature ensembles. Assessment of this framework is conducted using 30 synthetic experiments and 20 real case studies. Results show that the framework generates more robust parameter estimates, reduces the inaccuracy of flow predictions caused by poor quality climate data, and improves the reliability of flow predictions. The second stage adds flow ensemble to the previously developed framework to explicitly consider flow data uncertainty and thus completes the climate-flow ensemble based calibration framework. The complete framework can work with likelihood-free calibration methods. This thesis demonstrates the method of using the hydraulics-based Bayesian rating curve uncertainty estimation method (BaRatin) (Le Coz et al., 2014) to generate flow ensemble. The continuous ranked probability score (CRPS) is taken as an objective function of the framework to compare the scalar model prediction with the measured flow ensemble. The framework performance is assessed based on 10 case studies. Results show that explicit consideration of flow data uncertainty maintains the accuracy and slightly improves the reliability of flow predictions, but compared with climate data uncertainty, flow data uncertainty plays a minor role of improving flow predictions. Regarding streamflow forecasting applications, this thesis contributes by improving the treatment of measured climate data uncertainty in the ensemble Kalman filter (EnKF) data assimilation. Similar as in model calibration, past studies usually use assumed statistical error models to perturb climate data in the EnKF. In data assimilation, the hyper-parameters of the statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of climate data uncertainty estimation in the EnKF, this thesis proposes the direct use of existing climate ensemble products to derive climate ensembles. The N15 dataset is used here to generate 100-member precipitation and temperature ensembles. The N15 generated climate ensembles are compared with the carefully tuned hyper-parameter generated climate ensembles in ensemble flow forecasting over 20 catchments. Results show that the N15 generated climate ensemble yields improved or similar flow forecasts than hyper-parameter generated climate ensembles. Therefore, it is possible to eliminate the time-consuming climate relevant hyper-parameter tuning from the EnKF by using existing ensemble climate products without losing flow forecast performance. After finishing the above research, a robust hydrologic modeling approach is built by using the thesis developed model calibration and data assimilation methods. The last contribution of this thesis is validating such a robust hydrologic model in ensemble flow forecasting via comparison with the use of traditional multiple hydrologic models. The robust single-model forecasting system considers parameter and climate data uncertainty and uses the N15 dataset to perturb historical climate in the EnKF. In contrast, the traditional multi-model forecasting system does not consider parameter and climate data uncertainty and uses assumed statistical error models to perturb historical climate in the EnKF. The comparison study is conducted on 20 catchments and reveal that the robust single hydrologic model generates improved ensemble high flow forecasts. Therefore, robust single model is definitely an advantage for ensemble high flow forecasts. The robust single hydrologic model relieves modelers from developing multiple (and often distributed) hydrologic models for each watershed in their operational ensemble prediction system.
Author: Hydrologic Engineering Center (U.S.)
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages :
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Author: Evison Kapangaziwiri
Publisher: LAP Lambert Academic Publishing
ISBN: 9783844387377
Category :
Languages : en
Pages : 188
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Book Description
Reliable quantification of available water resources is an important prerequisite for sustainable management and development. However, the data required for adequate resource estimation are usually not available due to inadequate and shrinking measurement networks. Hydrologic models are the standard tool used to generate continuous estimates of stream flow and regionalisation has been the traditional approach to model application in poorly gauged basins. The classical approach to regionalization is to calibrate against naturalized observed data, identify hydrologically similar basins, and then transfer calibrated parameter sets to the ungauged basins. One of the problems related to this approach is the relatively few basins for calibration in southern Africa (due to data quality and availability problems), resulting in huge uncertainties in the prediction process. This study proposes an a priori process to directly estimate parameters of the Pitman monthly rainfall runoff model based on physical basin characteristics. The approach is tested on selected basins in southern African.
Author: David T. Ford
Publisher:
ISBN:
Category : Flood forecasting
Languages : en
Pages : 20
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Book Description
Computer program HEC-1, a precipitation-runoff model widely used throughout the United States, includes the capability to estimate automatically any of twelve parameters necessary to model the precipitation-runoff process and the channel routing process. The parameter estimation scheme employs Newton's method to minimize a weighted sum of squares of differences between observed and computed hydrograph values. Applications of this parameter estimation procedure are presneted, and typical steps of the procedure for deterimining optimal parameter estimates are outlined. Recent efforts to improve the estimation algorithm and recent use of the calibration capability to update sequentially parameter estimates in a flood forecasting application are discussed. (Author).
Author: Md Nazrul Islam
Publisher:
ISBN:
Category :
Languages : en
Pages : 272
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Book Description
Author: William J. Charley
Publisher:
ISBN:
Category : HEC-HMS (Computer program)
Languages : en
Pages : 16
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Book Description
