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 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 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 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 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 Sensitivity Analysis in Earth Observation Modelling written by George P. Petropoulos and published by Elsevier. This book was released on 2016-10-07 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling. Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement. Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling Provides readers with a roadmap for directing future efforts Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow

Download or read book Calibration and Sensitivity Analysis of the Continuous Runoff Simulation Model Storm written by Switzerland. Ecole Polytechnique Federale De Lausanne. Institut De Genie Rural and published by . This book was released on 1978 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Calibration and Sensitivity Analysis of the Continuos Runoff Simulation Model Storm written by Jean-Luc Sautier and published by . This book was released on 1977 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sensitivity based Guided Automatic Calibration of Hydrological Models written by Mohammad Semnani and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new method for efficient calibration of complex hydrological models that combines Dynamically Dimensioned Search (DDS) global optimization algorithm with Global Sensitivity Analysis (GSA) methods is introduced. This approach, which is called sensitivity-informed DDS, utilizes sensitivity indices to increase the probability of perturbation for the most sensitive parameters, while giving low chance to least sensitive ones. This feature improves the efficiency and effectiveness of optimization by finding good quality solutions in a shorter time. Three different implementations of sensitivity-informed DDS are considered. The first approach is named as GSA↔DDS, in which GSA toolboxes (Morris or Sobol) are performed initially and throughout the optimization process to constantly update the sensitivity information. The second approach is called GSA→DDS. In this method, the GSA methods are only performed initially to include the results of GSA within optimization process. The final implementation is called VARS→DDS. In this method, to enhance the efficiency of sensitivity analysis and optimization, VARS toolbox is performed outside the optimization to provide the sensitivity information. The performances of GSA↔DDS, GSA→DDS and VARS→DDS are compared with original DDS by solving various optimization problems (test functions and model calibration case studies). According to the results, when calibrating complex hydrological models with enough computational budget, VARS→DDS is significantly more efficient and effective than original DDS. However, the results also show that GSA→DDS and GSA↔DDS methods do not substantially improve the convergence rate and the final best solution compared to DDS. Thus, VARS→DDS is the recommended approach for sensitivity-informed DDS in calibration of distributed and semi-distributed models, when enough computational resources are available.

Download or read book Stochastic Modeling and Uncertainty Assessment for Watershed Water Quality Management written by Yi Zheng and published by . This book was released on 2007 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex watershed water quality models have been increasingly used to support Total Maximum Daily Load (TMDL) development. However, systematic approaches for addressing the significant simulation uncertainty are lacking. For TMDLs supported by complex watershed models, defining the margin of safety (MOS) component through a rigorous uncertainty analysis remains a significant challenge. This study aimed to develop (1) a systematic approach of uncertainty analysis for complex watershed water quality models in the watershed management context; and (2) a framework for defining the MOS with an explicit consideration of uncertainty and degree of protection. A global sensitivity analysis technique was first applied to select critical model parameters. A framework for sources of uncertainty and their interactions was built. Based on this framework, Generalized Likelihood Uncertainty Estimation (GLUE) was initially evaluated as a potential approach for conducting stochastic simulation and uncertainty analysis for complex watershed models. The limitations of GLUE became evident, which led to the development of a new Bayesian approach, Management Objectives Constrained Analysis of Uncertainty (MOCAU). The concept Compliance of Confidence (CC) was then introduced to bridge the gap between modeling uncertainty and MOS. An optimization model was also developed for cost-minimized TMDLs. This study used WARMF as an example of a complex watershed model and constructed a synthetic watershed for developing and testing methodologies. The methodologies were also implemented to study the diazinon TMDL in the Newport Bay watershed (southern California). This research contributes to the theory of stochastic watershed water quality modeling, as well as to the practices of managing watershed water quality.

Download or read book Integrated Sensitivity Analysis Calibration and Uncertainty Propagation Analysis Approaches for Supporting Hydrological Modeling written by Hongjing Wu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

Download or read book Pest Control Variable Sensitivity Analysis for Calibration of an IWFM Groundwater Model written by Sebastien Lazarus Poore and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The parameter estimation and calibration process of hydrological models is inherently time consuming and computationally demanding. The PEST (Parameter ESTimation) suite of software is a robust program used for automatically calibrating numerical models such as groundwater models built with IWFM (Integrated Water Flow Model.) Typically, representative parameter values are desired by the hydrological modeler, such as hydraulic conductivity, storativity, and specific yield. To facilitate this process, the modeler provides PEST with a set of field observations, including groundwater heads and streamflow measurements. These observations are used by PEST to compare the model results found from estimated parameter values, to the actual, physical observations. But altering the variables in the PEST control file can greatly affect the outcome of calibration runs. To determine the effects of each of these variables in the PEST control file on the model calibration process, a sensitivity analysis is performed using a synthetic groundwater model developed in IWFM. A set of “observations” is developed after running the synthetic groundwater model, which are then given to PEST to attempt to find the solution to the inverse problem. The effects of altering different control variables in the PEST control file is then explored, by seeing how altering these variables affects the solution to the inverse problem.

Download or read book Water Resource Systems Planning and Management written by Daniel P. Loucks and published by Springer. This book was released on 2017-03-02 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.

Download or read book Flood Risk Assessment and Management written by Andreas H. Schumann and published by Springer Science & Business Media. This book was released on 2011-01-04 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flood catastrophes which happened world-wide have shown that it is not sufficient to characterize the hazard caused by the natural phenomenon "flood" with the well-known 3M-approach (measuring, mapping and modelling). Due to the recent shift in paradigms from a safety oriented approach to risk based planning it became necessary to consider the harmful impacts of hazards. The planning tasks changed from attempts to minimise hazards towards interventions to reduce exposure or susceptibility and nowadays to enhance the capacities to increase resilience. Scientific interest shifts more and more towards interdisciplinary approaches, which are needed to avoid disaster. This book deals with many aspects of flood risk management in a comprehensive way. As risks depend on hazard and vulnerabilities, not only geophysical tools for flood forecasting and planning are presented, but also socio-economic problems of flood management are discussed. Starting with precipitation and meteorological tools to its forecasting, hydrological models are described in their applications for operational flood forecasts, considering model uncertainties and their interactions with hydraulic and groundwater models. With regard to flood risk planning, regionalization aspects and the options to utilize historic floods are discussed. New hydrological tools for flood risk assessments for dams and reservoirs are presented. Problems and options to quantify socio-economic risks and how to consider them in multi-criteria assessments of flood risk planning are discussed. This book contributes to the contemporary efforts to reduce flood risk at the European scale. Using many real-world examples, it is useful for scientists and practitioners at different levels and with different interests.

Download or read book Water Resources Systems Analysis written by Mohammad Karamouz and published by CRC Press. This book was released on 2003-06-27 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on conflict resolution, Water Resources Systems Analysis discusses systematic approaches to the mathematical modeling of various water resources issues, which helps decision-makers allocate water effectively and efficiently. Readers will gain an understanding of simulation, optimization, multi-criterion-decision-making, as well as engineer

Download or read book Pre test Predictions written by T.E. Sicking and published by . This book was released on 1961 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: