Download or read book Modeling Automated Parameter Calibration and Sensitivity Analysis of a Watershed Model of the Shaw Road Basin written by Alexandre Daniel Remnek and published by . This book was released on 2003 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Calibration of Watershed Models written by Qingyun Duan and published by John Wiley & Sons. This book was released on 2003-01-10 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Uncertainty and Sensitivity Analysis for Watershed Models with Calibrated Parameters written by Seunguk Lee and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis provides a critique and evaluation of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, and provides an appraisal of sensitivity analysis methods for watershed models with calibrated parameters. The first part of this thesis explores the strengths and weaknesses of the GLUE methodology with commonly adopted subjective likelihood measures using a simple linear watershed model. Recent research documents that the widely accepted GLUE procedure for describing forecasting precision and the impact of parameter uncertainty in rainfall-runoff watershed models fails to achieve the intended purpose when used with an informal likelihood measure (Christensen, 2004; Montanari, 2005; Mantovan and Todini, 2006; Stedinger et al., 2008). In particular, GLUE generally fails to produce intervals that capture the precision of estimated parameters, and the distribution of differences between predictions and future observations. This thesis illustrates these problems with GLUE using a simple linear rainfall-runoff model so that model calibration is a linear regression problem for which exact expressions for prediction precision and parameter uncertainty are well known and understood. The results show that the choice of a likelihood function is critical. A likelihood function needs to provide a reasonable distribution for the model errors for the statistical inference and resulting uncertainty and prediction intervals to be valid. The second part of this thesis discusses simple uncertainty and sensitivity analysis for watershed models when parameter estimates result form a joint calibration to observed data. Traditional measures of sensitivity in watershed modeling are based upon a framework wherein parameters are specified externally to a model, so one can independently investigate the impact of uncertainty in each parameter on model output. However, when parameter estimates result from a joint calibration to observed data, the resulting parameter estimators are interdependent and different sensitivity analysis procedures should be employed. For example, over some range, evaporation rates may be adjusted to correct for changes in a runoff coefficient, and vice versa. As a result, descriptions of the precision of such parameters may be very large individually, even though their joint response is well defined by the calibration data. These issues are illustrated with the simple abc watershed model. When fitting the abc watershed model to data, in some cases our analysis explicitly accounts for rainfall measurement errors so as to adequately represent the likelihood function for the data given the major source of errors causing lack of fit. The calibration results show that the daily precipitation from one gauge employed provides an imperfect description of basin precipitation, and precipitation errors results in correlation among flow errors and degraded the goodness of fit.
Download or read book Multifaceted Univariate Sensitivity Analysis and Automatic Calibration for Complex Watershed Simulation Models written by Ryan Clark Fleming and published by . This book was released on 2007 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Effective Groundwater Model Calibration written by Mary C. Hill and published by John Wiley & Sons. This book was released on 2006-08-25 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and guidelines for developing and using mathematical models Turn to Effective Groundwater Model Calibration for a set of methods and guidelines that can help produce more accurate and transparent mathematical models. The models can represent groundwater flow and transport and other natural and engineered systems. Use this book and its extensive exercises to learn methods to fully exploit the data on hand, maximize the model's potential, and troubleshoot any problems that arise. Use the methods to perform: Sensitivity analysis to evaluate the information content of data Data assessment to identify (a) existing measurements that dominate model development and predictions and (b) potential measurements likely to improve the reliability of predictions Calibration to develop models that are consistent with the data in an optimal manner Uncertainty evaluation to quantify and communicate errors in simulated results that are often used to make important societal decisions Most of the methods are based on linear and nonlinear regression theory. Fourteen guidelines show the reader how to use the methods advantageously in practical situations. Exercises focus on a groundwater flow system and management problem, enabling readers to apply all the methods presented in the text. The exercises can be completed using the material provided in the book, or as hands-on computer exercises using instructions and files available on the text's accompanying Web site. Throughout the book, the authors stress the need for valid statistical concepts and easily understood presentation methods required to achieve well-tested, transparent models. Most of the examples and all of the exercises focus on simulating groundwater systems; other examples come from surface-water hydrology and geophysics. The methods and guidelines in the text are broadly applicable and can be used by students, researchers, and engineers to simulate many kinds systems.
Download or read book Distributed Watershed Modeling of Shaw Road Basin written by Ziyin Shen and published by . This book was released on 2003 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Watershed Models written by Vijay P. Singh and published by CRC Press. This book was released on 2010-09-28 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Watershed modeling is at the heart of modern hydrology, supplying rich information that is vital to addressing resource planning, environmental, and social problems. Even in light of this important role, many books relegate the subject to a single chapter while books devoted to modeling focus only on a specific area of application. Recognizing the
Download or read book SENSITIVITY ANALYSIS AND CALIBRATION OF THE SWAT MODEL FOR IMPROVED PEAK FLOW SIMULATION written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Climate change and anthropogenic activities create uncertainty with respect to future hydrological conditions, and thus pose challenges in predicting streamflow, particularly the magnitude of extreme events. Several studies have focused on understanding future flood risk under climate and land use/land cover (LULC) changes using hydrological models. In addition to biases from climate data, biases from hydrological models, especially on peak flow simulations were reported to be large (usually underestimations). This could limit the dependability of flood risk projections and their applicability for future decision making. This research study investigates techniques and approaches for improved simulation of streamflows with focus on peak flows using the Soil and Water Assessment Tool (SWAT) for three case study watersheds. In particular, evaluations include choice of criteria for sensitivity analysis and parameter identification, choice of objective function for calibration, and impact assessment when calibrated models are applied for periods with alternate climate and physical characteristics. For ease of calibration, sensitivity analysis is crucial to identify relevant parameters; however, it can provide different parameter sets based upon the implemented sensitivity criteria. Herein, four sensitivity criteria, namely the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), modified R2 (bR2), and percent bias (PBIAS) were compared in watersheds of contrasting climate, hydrology, and land cover. For rainfall-runoff dominated agricultural watersheds, NSE, bR2, and R2 produced relatively similar parameter sets, and thus these criteria can be used individually or together for the purposes of sensitivity analysis, especially if peak flows are the target. For a snowmelt dominated forested watershed, R2 was found to be the best sensitivity criterion to identify parameters affecting peak flows. Moreover, for this watershed, sensitivity analysis and light calibration of snowmelt related parameters separately followed by calibration of the hydrological parameters resulted in improved flow simulations compared to the default approach where all parameters were analyzed together. The ability of models calibrated to a given set of climate and LULC data to simulate flood risk under altered conditions was assessed in each watershed by applying parameters calibrated for 2002-2005 to 1970-1999. Simulated annual maximum daily flows for the latter period were used to estimate the instantaneous annual maximum flow (AMF) series, and the impact of altered parameter values on the resulting flood distribution was assessed via a one-at-a-time sensitivity analysis. As anticipated, AMFs in the agricultural rainfall-runoff dominated watersheds were sensitive to changes in runoff related parameters, whereas AMFs in the forested snowmelt and dominated watershed were sensitive to changes in snowmelt related parameters. Alteration of the bank storage recession constant was found to significantly affect AMFs in all three watersheds. It was observed that simulation of the flood risk distribution under altered climate can be improved by modifying snow related parameters based upon the observed change in temperature from the calibration period. In flood risk studies with projected urbanization and expansion of agricultural areas, the curve number parameter should be adjusted by the proportion of change relative to the baseline (or calibration) period.
Download or read book Uncertainty and Sensitivity Analyses for Watershed Models written by Jennifer Benaman and published by . This book was released on 2003 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances In Data based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and published by World Scientific. This book was released on 2010-08-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Phosphorus Loading from a Monitored Dairy Farm Landscape written by Wells Dean Hively and published by . This book was released on 2004 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Improved Framework for Watershed Discretization and Model Calibration written by Amin Haghnegahdar and published by . This book was released on 2015 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale (~103-106 km2) physically-based distributed hydrological models have been used increasingly, due to advances in computational capabilities and data availability, in a variety of water and environmental resources management, such as assessing human impacts on regional water budget. These models inevitably contain a large number of parameters used in simulation of various physical processes. Many of these parameters are not measurable or nearly impossible to measure. These parameters are typically estimated using model calibration, defined as adjusting the parameters so that model simulations can reproduce the observed data as close as possible. Due to the large number of model parameters, it is essential to use a formal automated calibration approach in distributed hydrological modelling. The St. Lawrence River Basin in North America contains the largest body of surface fresh water, the Great Lakes, and is of paramount importance for United States and Canada. The Lakes' water levels have huge impact on the society, ecosystem, and economy of North America. A proper hydrological modelling and basin-wide water budget for the Great Lakes Basin is essential for addressing some of the challenges associated with this valuable water resource, such as a persistent extreme low water levels in the lakes. Environment Canada applied its Modélisation Environnementale-Surface et Hydrologie (MESH) modelling system to the Great Lakes watershed in 2007. MESH is a coupled semi-distributed land surface-hydrological model intended for various water management purposes including improved operational streamflow forecasts. In that application, model parameters were only slightly adjusted during a brief manual calibration process. Therefore, MESH streamflow simulations were not satisfactory and there was a considerable need to improve its performance for proper evaluation of the MESH modelling system. Collaborative studies between the United States and Canada also highlighted the need for inclusion of the prediction uncertainty in modelling results, for more effective management of the Great Lakes system. One of the primary goals of this study is to build an enhanced well-calibrated MESH model over the Great Lakes Basin, particularly in the context of streamflow predictions in ungauged basins. This major contribution is achieved in two steps. First, the MESH performance in predicting streamflows is benchmarked through a rather extensive formal calibration, for the first time, in the Great Lakes Basin. It is observed that a global calibration strategy using multiple sub-basins substantially improved MESH streamflow predictions, confirming the essential role of a formal model calibration. At the next step, benchmark results are enhanced by further refining the calibration approach and adding uncertainty assessment to the MESH streamflow predictions. This enhancement was mainly achieved by modifying the calibration parameters and increasing the number of sub-basins used in calibration. A rigorous multi-criteria comparison between the two experiments confirmed that the MESH model performance is indeed improved using the revised calibration approach. The prediction uncertainty bands for the MESH streamflow predictions were also estimated in the new experiment. The most influential parameters in MESH were also identified to be soil and channel roughness parameters based on a local sensitivity test. One of the main challenges in hydrological distributed modelling is how to represent the existing spatial heterogeneity in nature. This task is normally performed via watershed discretization, defined as the process of subdividing the basin into manageable “hydrologically similar” computational units. The model performance, and how well it can be calibrated using a limited budget, largely depends on how a basin is discretized. Discretization decisions in hydrologic modelling studies are, however, often insufficiently assessed prior to model simulation and parameter. Few studies explicitly present an organized and objective methodology for assessing discretization schemes, particularly with respect to the streamflow predictions in ungauged basins. Another major goal of this research is to quantitatively assess watershed discretization schemes for distributed hydrological models, with various level of spatial data aggregation, in terms of their skill to predict flows in ungauged basins. The methodology was demonstrated using the MESH model as applied to the Nottawasaga river basin in Ontario, Canada. The schemes differed from a simple lumped scheme to more complex ones by adding spatial land cover and then spatial soil information. Results reveal that calibration budget is an important factor in model performance. In other words, when constrained by calibration budget, using a more complex scheme did not necessarily lead to improved performance in validation. The proposed methodology was also implemented using a shorter sub-period for calibration, aiming at substantial computational saving. This strategy is shown to be promising in producing consistent results and has the potential to increase computational efficiency of this comparison framework. The outcome of this very computationally intensive research, i.e., the well-calibrated MESH model for the Great Lakes and all the final parameter sets found, are now available to be used by other research groups trying to study various aspects of the Great Lakes System. In fact, the benchmark results are already used in the Great Lakes Runoff Intercomparison Project (GRIP). The proposed comparison framework can also be applied to any distributed hydrological model to evaluate alternative discretization schemes, and identify one that is preferred for a certain case.
Download or read book Application of Precipitation runoff Modeling System to Ward Creek Drainage Basin Lake Tahoe California written by Yasar Inci Tekeli and published by . This book was released on 1999 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Calibration and Sensitivity Analysis of the Continuous Runoff Simulation Model STORM written by Jean Luc Sautier and published by . This book was released on 1978 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Models of Watershed Hydrology written by Vijay P. Singh and published by . This book was released on 1995 with total page 1150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical Models of Large Watershed Hydrology written by Vijay P. Singh and published by Water Resources Publication. This book was released on 2002 with total page 930 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive account of some of the most popular models of large watershed hydrology ~~ of interest to all hydrologic modelers and model users and a welcome and timely edition to any modeling library
Download or read book Hydrologic Modeling of Small Watersheds written by Charles Thomas Haan and published by . This book was released on 1982 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt:
