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 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 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 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 Model Calibration and Parameter Estimation written by Ne-Zheng Sun and published by Springer. This book was released on 2015-08-14 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.

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 Modeling Water Resources Management at the Basin Level written by Ximing Cai and published by Intl Food Policy Res Inst. This book was released on 2006-01-01 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report develops an integrated economic-hydrologic river basin model and applies it to the Maipo River Basin in central Chile. Policy simulations based on the modeling framework can serve as a guide for water resource managers and policymakers in designing appropriate water policies and establishing reform priorities for water resource allocation. Alternative analyses undertaken for the Maipo basin-a mature water economy with limited resources and competition for water across all water-using sectors-offer new insights into the changing relationships between irrigation system and basin-level water use efficiencies. They also show how these changing relationships affect farm incomes and environmental impacts. Simulations also provide new results on the role that the trading of water use rights can play in maintaining farm production levels, enhancing farmer incomes, and increasing water use efficiencies.

Download or read book Root Zone Water Quality Model written by Lajpat Ahuja and published by Water Resources Publication. This book was released on 2000 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication comes with computer software and presents a comprehensive simulation model designed to predict the hydrologic response, including potential for surface and groundwater contamination, of alternative crop-management systems. It simulates crop development and the movement of water, nutrients and pesticides over and through the root zone for a representative unit area of an agricultural field over multiple years. The model allows simulation of a wide spectrum of management practices and scenarios with special features such as the rapid transport of surface-applied chemicals through macropores to deeper depths and the preferential transport of chemicals within the soil matrix via mobile-immobile zones. The transfer of surface-applied chemicals (pesticides in particular) to runoff water is also an important component.

Download or read book Parameter Sensitivity and Uncertainty Analysis in Simplified Conceptual Urban Drainage Models written by Cintia Brum Siqueira Dotto and published by . This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stormwater models are powerful tools to aid the planning, design and performance of different stormwater management strategies. Although these models provide a great platform for decision making, they all have an intrinsic level of uncertainty. Little is understood about the sources and magnitude of this uncertainty, which could be due to the errors in measured data (input and calibration data) and/or due to the model itself. To better understand these sources and their impacts on the model predictions, robust model calibration and sensitivity analysis should be performed. The methodologies used for such an exercise should not only be able to provide an assessment of the uncertainties in the model's parameter values and an evaluation of the confidence level of the model's predictions, but also be able to identify and propagate the different sources of uncertainties. The main aim of this research project is to assess uncertainties in conceptual urban stormwater flow and pollution generation models, with different levels of complexity, by evaluating the impact of different sources of uncertainties on the model predictions and parameter sensitivity. The research focuses on three main steps: (i) identifying suitable global sensitivity analysis method(s) to perform parameter calibration, model sensitivity and uncertainty analysis in stormwater models; (ii) exploring parameter calibration, model sensitivity and the resulting predictive uncertainties in models with different level of complexities; and, (iii) investigating the impact of measured input and calibration data uncertainty on the performance, sensitivity and predictive uncertainty of stormwater models. Four methods were applied for calibration, sensitivity and uncertainty analysis of a simple stormwater (quantity and quality) model: one is a formal Bayesian approach, and three are methods based on Monte Carlo simulations coupled with different sampling and acceptance criteria. While the application of the four methods generated similar posterior parameter distributions and predictive uncertainty, results indicated that the selection of the most appropriate method is a trade-off between the need for a strong theory-based description of uncertainty (but limited by the requirements on prior knowledge), simplicity (but limited by the subjectivity) and computational efficiency (also affected by subjectivity). The results also suggested that modellers should select the method which is most suitable for the system they are modelling, their skill/knowledge level, the available information, and the purpose of their study. Further analysis of the application of the Bayesian approach verified the potential of the method to assess urban drainage models (with different level of complexities) in urban catchments of different sizes and land-use types. The tested Bayesian approach was selected to be used in the remaining activities of this research.The likelihood function in the applied Bayesian approach assumes that the model errors (residuals) are normally distributed. This study demonstrated that this assumption is often not met in stormwater modelling (i.e. model residuals are not normally distributed), and therefore, the data was transformed (Box-Cox) to ensure the normality of the model residuals. The main finding was that the parameter sensitivity varied significantly between the scenarios in which the normality assumption of the residuals was verified or not. The main reason for this being the fact that the data transformation method to meet the assumption altered the intrinsic content of the measured data, which then influenced the emphasis on various parts of the hydrograph. The Bayesian approach was used to assess two conceptual catchment rainfall runoff models (MUSIC, which simulates runoff from both impervious and pervious areas as a series of reservoirs; and, KAREN that simulates runoff from impervious surfaces using the time-area method) and few simple stormwater quality models (empirical regressions and build-up/wash-off based models). Results from parameter calibration and sensitivity analysis of the rainfall runoff models demonstrated that the effective impervious fraction is the main parameter governing the prediction of runoff in urbanised catchments. Other key parameters are those related to the time of concentration. Indeed, the analysis indicated that the pervious area parameters play a secondary role when modelling highly urbanised catchments, which implies that the tested models could be simplified. The uncertainty analysis showed that the total predictive uncertainty bands (i.e. the total uncertainty derived from the specific modelling application) was considerably larger than the uncertainty bands contributed from parameter uncertainty alone, indicating that there are other prominent sources of uncertainty for these models. The water quality models were shown to be 'ill-posed' and unable to reproduce the pollutant processes in the catchment. The impact of both input and calibration data errors on the parameter sensitivity and predictive uncertainty was evaluated by means of propagating these errors through the selected urban stormwater model (rainfall runoff model KAREN coupled with a build-up/wash-off water quality model). It was found that random errors in measured data had minor impact on the model performance and sensitivity. Systematic errors in input and calibration data impacted the parameter distributions (e.g. changed their shapes and location of peaks). In most of the systematic error scenarios (especially those where uncertainty in input and calibration data was represented using 'best-case' assumptions), the errors in measured data were fully compensated by the parameters. For example, when rainfall was systematically under or overestimated, the effective impervious area parameter varied systematically to compensate for the changes in the input data. Parameters were unable to compensate in some of the scenarios where the systematic uncertainty in the input and calibration data were represented using extreme worst-case scenarios. As such, in these few worst case scenarios, the model's performance was reduced considerably. Systematic errors in the calibration data error did not significantly impact the parameter probability distributions of the water quality model, mainly because the model cannot even reproduce TSS concentrations when the 'true' data is used. This finding suggested that the current main limitation in water quality modelling is related to poor model structure, and not to errors in measured data.This research provides a comprehensive study of the propagation of different sources of uncertainties through stormwater models. It identifies how the different uncertainty sources impact on parameter sensitivity and the predictive uncertainty. In addition, the analysis of model parameters and their interactions provides practical recommendations for refining and further developing stormwater rainfall runoff and pollution generation models.

Download or read book Bayesian Optimization and Uncertainty Analysis of Complex Environmental Models with Applications in Watershed Management written by Able Mashamba and published by . This book was released on 2010 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents results of research in the development, testing and application of an automated calibration and uncertainty analysis framework for distributed environmental models based on Bayesian Markov chain Monte Carlo (MCMC) sampling and response surface methodology (RSM) surrogate models that use a novel random local fitting algorithm. Typical automated search methods for optimization and uncertainty assessment such as evolutionary and Nelder-Mead Simplex algorithms are inefficient and/or infeasible when applied to distributed environmental models, as exemplified by the watershed management scenario analysis case study presented as part of this dissertation. This is because the larger numbers of non-linearly interacting parameters and the more complex structures of distributed environmental models make automated calibration and uncertainty analysis more computationally demanding compared to traditional basin-averaged models. To improve efficiency and feasibility of automated calibration and uncertainty assessment of distributed models, recent research has been focusing on using the response surface methodology (RSM) to approximate objective functions such as sum of squared residuals and Bayesian inference likelihoods. This dissertation presents (i) results on a novel study of factors that affect the performance of RSM approximation during Bayesian calibration and uncertainty analysis, (ii) a new 'random local fitting' (RLF) algorithm that improves RSM approximation for large sampling domains and (iii) application of a developed automated uncertainty analysis framework that uses MCMC sampling and a spline-based radial basis approximation function enhanced by the RLF algorithm to a fully-distributed hydrologic model case study. Using the MCMC sampling and response surface approximation framework for automated parameter and predictive uncertainty assessment of a distributed environmental model is novel. While extended testing of the developed MCMC uncertainty analysis paradigm is necessary, the results presented show that the new framework is robust and efficient for the case studied and similar distributed environmental models. As distributed environmental models continue to find use in climate change studies, flood forecasting, water resource management and land use studies, results of this study will have increasing importance to automated model assessment. Potential future research from this dissertation is the investigation of how model parameter sensitivities and inter-dependencies affect the performance of response surface approximation.

Download or read book Hydrologic and Water Quality Model Evaluation With Global Sensitivity Analysis written by Yogesh P. Khare and published by . This book was released on 2014 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed parameter samples was merged with the often-used goal of maximizing sample spread. The evaluation of this strategy with benchmark strategies indicated that it was more than an order of magnitude computationally faster for high dimensional models and that it also somewhat improves parameter screening. The last objective was aimed at the evaluation of the routing and pollutant attenuation module of the Watershed Assessment Model (WAM). GSA/UA showed that WAM was most sensitive to empirical/conceptual parameters, indicating their importance in the model calibration/validation process and the need for monitoring efforts to verify the selection of these parameters. As the first formal evaluation of WAM by GSA/UA, the results obtained in this work are a valuable contribution to the application of WAM to addressing water quality issues in the state of Florida.

Download or read book Sensitivity Analysis and Parameter Estimation for the APEX Model on Runoff Sediments and Phosphorus written by Yi Jiang and published by . This book was released on 2016 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis is essential for the hydrologic models to help gain insight into model’s behavior, and assess the model structure and conceptualization. Parameter estimation in the distributed hydrologic models is difficult due to the high-dimensional parameter spaces. Sensitivity analysis identified the influential and non-influential parameters in the modeling process, thus it will benefit the calibration process. This study identified, applied and evaluated two sensitivity analysis methods for the APEX model. The screening methods, the Morris method, and LH-OAT method, were implemented in the experimental site in North Carolina for modeling runoff, sediment loss, TP and DP losses. At the beginning of the application, the run number evaluation was conducted for the Morris method. The result suggested that 2760 runs were sufficient for 45 input parameters to get reliable sensitivity result. Sensitivity result for the five management scenarios in the study site indicated that the Morris method and LH-OAT method provided similar results on the sensitivity of the input parameters, except the difference on the importance of PARM2, PARM8, PARM12, PARM15, PARM20, PARM49, PARM76, PARM81, PARM84, and PARM85. The results for the five management scenarios indicated the very influential parameters were consistent in most cases, such as PARM23, PARM34, and PARM84. The “sensitive” parameters had good overlaps between different scenarios. In addition, little variation was observed in the importance of the sensitive parameters in the different scenarios, such as PARM26. The optimization process with the most influential parameters from sensitivity analysis showed great improvement on the APEX modeling performance in all scenarios by the objective functions, PI1, NSE, and GLUE.

Download or read book Modeling Parameter Optimization And Ecohydrologic Assessment Of Watershed Systems written by Xuan Yu and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrated watershed models describe the land-phase of hydrologic cycles by coupling processes such as canopy interception, infiltration, recharge, evapotranspiration, overland flow, vadose zone flow, groundwater flow, and channel routing. This modeling scheme serves as an important strategy for understanding the moisture redistribution processes across the watershed and river-basin landscape. For example, the Penn State Integrated Hydrologic Model (PIHM) has successfully been applied to explain the impacts of antecedent soil moisture on peak streamflow and timing. However, due to the heavy computational cost of solving integrated models with complex model structure, efficient parameter estimation for PIHM is a major computational and algorithmic challenge. The focus of this dissertation has four main themes: (1) develop an efficient calibration strategy for PIHM; (2) develop a weighted-objective calibration scheme for multi-variable distributed parameters (e.g., streamflow, water table depth, and eddy flux data); (3) test the parameter-estimation process for spatial shallow groundwater calibration of PIHM using national wetland geospatial data (National Wetland Inventory: NWI); (4) extend the capabilities of PIHM for linking vegetation dynamics from an ecosystem model and evaluating the importance of vegetation growth in water balance studies.The temporal and geospatial complexity of the data requirements for integrated and fully coupled catchment models increases the difficulty of applying parameter optimization in real watershed applications. In this research, a new strategy known as partition calibration was proposed to enable the automatic calibration of PIHM. The concept can be thought of as a "divide-and-conquer algorithm," where the parameter space is divided into two or more sub-problems that can be solved sequentially. The first partition of the parameter vector is divided according to the two dominant time-scales of catchment hydrological processes: 1) event-scale hydrologic response parameters; and 2) seasonal-scale response parameters. Once divided, the event-scale group parameters and seasonal-scale group parameters are then calibrated sequentially. The second partition focused on the separation of the total calibration objective onto multiple targets to predict each observed hydrological variable. The "informativeness" of each calibration target was defined in terms of a weighted objective function. Application of the scheme suggested the use of an informativeness-based partitioning of streamflow, groundwater, and ET parameters and demonstrated that partition calibration was superior to both single-objective calibration and un-weighted average multi-objective calibration.Applications of the PIHM were found to be efficient with the partition calibration strategy. The first PIHM application involves characterization of the freshwater wetland response to climate change at seven catchments within the Susquehanna River Basin. In this case, streamflow time series and geospatial mapping of wetlands in the National Wetland Inventory (NWI) were used to calibrate the model to capture the distributed groundwater and streamflow dynamics. After calibration, the model was applied to an IPCC climate change scenario (2046-2065), and the modeling results suggested that upland groundwater levels were more sensitive to climate change than water levels of wetlands in lower parts of the catchment, as expected. In the final part of this research, long-term modeling of PIHM compared the role of fixed seasonal variation in LAI (Leaf Area Index) and a fully dynamic vegetation growth model. The community ecosystem model BIOME-BGC was linked to PIHM to test the hypothesis that default monthly LAI values are sufficient to represent long-term water balances in a catchment. By comparing model results for fixed LAI and dynamic LAI, it was demonstrated that fixed LAI is not sufficient for capturing interannual variability of forest vegetation and water flow dynamics, especially as it relates to the onset and growth of forest.

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 Uncertainty Metrics for Coupled Watershed Models written by Geoff Parker and published by . This book was released on 2009 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hydrologic Modeling and Climate Change Study in the Upper Mississippi River Basin Using SWAT written by Manoj Jha and published by . This book was released on 2004 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation describes the modeling efforts on the Upper Mississippi River Basin (UMRB) using the Soil and Water Assessment Tool (SWAT) model. The main goal of this study is to apply the SWAT model to the UMRB to evaluate the model as a tool for agricultural policy analysis and climate change impact analysis. A sensitivity analysis using influence coefficient method was conducted for eight selected hydrologic input parameters to identify the most to the least sensitive parameters. Calibration and validation of SWAT were performed for the Maquoketa River Watershed for streamflow on annual and monthly basis. The model was then validated for the entire UMRB streamflow and evaluated for a climate change impact analysis. The results indicate that the UMRB hydrology is very sensitve to potential future climate changes. The impact of future climate change was then explored for the streamflow by using two 10-year scenario periods (1990 and 2040s) generated by introducing a regional climate model (RegCM2) to dynamically downscale global model (HadCM2) results. The combined GCM-RCM-SWAT model system produced an increase in future scenario climate precipitation of 21% with a resulting 50% increase in total water yield in the UMRB. Furthermore, evaluation of model-introduced uncertainties due to use of SWAT, GCM, and RCM models yielded the highest percentage bias (18%) for the GCM downscaling error. Building upon the above SWAT validation, a SWAT modeling framework was constructed for the entire UMRB, which incorporates more detailed input data and is designed to assess the effects of land use, climate, and soil conditions on streamflow and water quality. An application of SWAT is presented for the Iowa and Des Moines River watersheds within the modeling framework constructed for the UMRB. A scenario run where conservation tillage adoption increased to 100% found a small sediment reduction of 5.8% for Iowa River Watershed and 5.7% for Des Moines River Watershed. On per-acre basis, sediment reduction for Iowa and Des Moines River Watersheds was found to be 1.86 and 1.18 metric tons respectively. Furthermore an attempt to validate the model for the entire UMRB yielded strong annual results.

Download or read book Integrative Hydrological and Ecological Modeling for Regional Water Resources Management in the Yellow River Basin China written by Xianglian Li and published by . This book was released on 2008 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: