Download or read book Improving Multi reservoir Water Supply System Operation Using Ensemble Forecasting and Global Sensitivity Analysis written by Reza Limon and published by . This book was released on 2019 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this research is to improve the cost-effectiveness, reliability and resilience of water supply system operation through the utilization of ensemble forecasting and optimization of the operating policies. For the effective operation of a multi-reservoir water supply system skillful inflow and outflow forecasts are required. To that end, this research first assesses the value of medium-range ensemble precipitation forecasts generated with the HEFS developed by the US NWS in increasing skill and lead time of ensemble inflow forecasts. The skill of reservoir outflow forecasts is then assessed using three different models with varying complexity. Also assessed in the above is the relative importance of meteorological, hydrologic and reservoir modeling uncertainty in outflow forecasts. Lastly, the proposed policy modifications were optimized via global sensitivity analysis using the variance-based Sobol method to improve cost-effectiveness of the system operation further. The analysis results identify influential policies, assess their impact on the cost response of the system operation, and determine policy modifications. Main conclusions of this research follow below. Compared to using only the 72-hr RFC QPF, the use of the medium-range precipitation forecasts from GEFS increases skill and lead time of mean daily inflow forecasts from HEFS by up to 3 days for significant events. The HEFS-produced multi-daily inflow forecasts are significantly more skillful than the daily inflow forecasts, and extends the lead time of skillful forecasting further. It is demonstrated that the use of the HEFS-produced ensemble inflow forecasts results in significant savings in mean annual pumping cost, compared to the TRWD's current practice of inflow forecasting. Among the three reservoir models considered in this work, RiverWare provides the largest skill in MEFP-GEFS-forced outflow forecasts. It is shown that meteorological, hydrologic and reservoir modeling uncertainties are comparably importance in improving skill of reservoir outflow forecasts. However, the decomposition of total predictive uncertainty into the above three indicates that the relative importance varies significantly with lead time and among different reservoir models. Evaluation of reservoir outflow forecasts for specific large outflow events shows that, although the reservoir outflow forecasts forced by the HEFS inflow forecasts are not probabilistically unbiased for very large to extreme events, the ensemble spread of the outflow forecasts is generally able to encompass the observed pool elevation and outflow. As such, the HEFS inflow forecasts provide additional critical information not available from single-valued forecasts for risk-based decision making in reservoir operations.Among the five operating policies selected for considered for modification, only one or two exert large influence on the cost for a given year. The influential polices, however, vary very significantly from year to year. The cost response of the system to policy modifications is complex and shows large interannual variations. Hydroclimatic conditions, storage states, inflow, and demand largely determine the influential policies and their modifications. It is shown that annually-varying, or dynamic, determination of policy modifications offer significantly larger potential for cost savings than using optimized, but fixed, policy modifications.
Download or read book The Value of Using Hydrological Datasets for Water Allocation Decisions Earth Observations Hydrological Models and Seasonal Forecasts written by Alexander José Kaune Schmidt and published by CRC Press. This book was released on 2019-11-21 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: An increasing number of hydrological datasets from earth observations, hydrological models and seasonal forecasts have become available for water managers, consultants and the general public. These datasets are state-of-the-art products which are usually accessible online and may contribute to develop hydrological studies and support water resources management. However, the added value of these datasets has not been completely explored in decision-making processes. Research studies have assessed how well data can help in predicting climate, but there is a lack of knowledge on how well data can help in water allocation decisions. This work provides numerical tools, methods and results to evaluate the value of using hydrological datasets to support water allocation decisions at river basin and irrigation district scale. An integrated approach is used to predict climate, improve decisions and reduce negative impacts. Results show that investing in hydrological data with finer spatial and temporal resolution and longer periods of record improves water allocation decisions and reduces agricultural production loss in large irrigation schemes. Using river discharge data from hydrological models and global precipitation enhances irrigation area planning when little in-situ data is available. Moreover, using seasonal streamflow forecasts improves available water estimates resulting in better water allocation decisions. The framework was tested in Costa Rica, Colombia and Australia, but can be applied in any case study around the world.
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 Assimilation of Multi Sensor Data Into Numerical Hydrodynamic Models of Inland Water Bodies written by Amir Javaheri and published by . This book was released on 2016 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical models are effective tools for simulating complex physical processes such as hydrodynamic and water quality processes in aquatic systems. The accuracy of the model is dependent on multiple model parameters and variables that need to be calibrated and regularly updated to reproduce changing aquatic conditions accurately. Multi-sensor water temperature observations, such as remote sensing data and in situ monitoring technologies, can improve model accuracy by providing benefits of individual monitoring technology to the model updating process. In contrast to in-situ temperature sensors, remote sensing technologies (e.g., satellites) provide the benefit of collecting measurements with better X-Y spatial coverage. However, the temporal resolution of satellite data is limited comparing to in-situ measurements. Numerical models and all source of observations have large uncertainty coming from different sources such as errors of approximation and truncation, uncertain model inputs, error in measuring devices and etc. Data assimilation (DA) is able to sequentially update the model state variables by considering the uncertainty in model and observations and estimate the model states and outputs more accurately. Data Assimilation has been proposed for multiple water resources studies that require rapid employment of incoming observations to update and improve accuracy of operational prediction models. The usefulness of DA approaches in assimilating water temperature observations from different types of monitoring technologies (e.g., remote sensing and in-situ sensors) into numerical models of in-land water bodies (e.g., reservoirs, lakes, and rivers) has, however, received limited attention. Assimilating of water temperature measurements from satellites can introduce biases in the updated numerical model of water bodies because the physical region represented by these measurements do not directly correspond with the numerical model's representation of the water column. The main research objective of this study is to efficiently assimilate multi-sensor water temperature data into the hydrodynamic model of water bodies in order to improve the model accuracy. Four specific objectives were addressed in this work to accomplish the overall goal: * Objective 1: Propose a novel approach to address the representation challenge of model and measurements by coupling a skin temperature adjustment technique based on available air and in-situ water temperature observations, with an ensemble Kalman filter (EnKF) based data assimilation technique for reservoirs and lakes. * Objective 2: Investigate whether assimilation of remotely sensed temperature observations using the proposed data fusion approach can improve model accuracy with respect to in-situ temperature observations as well as remote sensing data. * Objective 3: Investigate a global sensitivity analysis tool that combines Latin-hypercube and one-factor-at-a-time sampling to investigate the most sensitive model inputs and parameters in calculating the water age and water temperature of shallow rivers. * Objective 4: Propose an efficient data assimilation framework to take the advantage of both monitoring technologies (e.g., remote sensing and in-situ measurements) to improve the model efficiency of shallow rivers. Results showed that the proposed adjustment approach used in this study for four-dimensional analysis of a reservoir provides reasonably accurate surface layer and water column temperature forecasts, in spite of the use of a fairly small ensemble. Assimilation of adjusted remote sensing data using ensemble Kalman Filter technique improved the overall root mean square difference between modeled surface layer temperatures and the adjusted remotely sensed skin temperature observations from 5.6 °C to 0.51 °C (i.e., 91% improvement). In addition, the overall error in the water column temperature predictions when compared with in-situ observations also decreased from 1.95 °C (before assimilation) to 1.42 °C (after assimilation), thereby, giving a 27% improvement in errors. In contrast, doing data assimilation without the proposed temperature adjustment would have increased this error to 1.98 °C (i.e., 1.5% deterioration). The most effective parameters to calculate water temperature were investigated and perturbed among the acceptable range to create the ensembles. Results show that water temperature is more sensitive to inflow temperature, air temperature, solar radiation, wind speed, flow rate, and wet bulb temperature respectively. Results also show in contrast to in-situ data assimilation, remote sensing data assimilation was able to effectively improve the spatial error of the model. Assimilation of in-situ observation improved the model efficiency at observation site. However, the model error increased by time and after less than two days, the model predictions of updated model were the same as base model before data assimilation. Hence, a maximum acceptable error between model and measurements was defined based on the application of model. Remote sensing data were assimilated into the model as they become available to improve the model accuracy for the entire river. In-situ data were also assimilated into the model when the error between model and observations exceeds the maximum error. Results showed that by assimilation of in-situ data one to three times per day, the average daily error reduced up to 58% comparing to situation that in-situ data were assimilated only once. In addition, the average spatial error reduced from 2.59 °C to 0.66 °C after assimilation of remote sensing data.
Download or read book Introduction to Optimization Analysis in Hydrosystem Engineering written by Ehsan Goodarzi and published by Springer Science & Business Media. This book was released on 2014-02-06 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the basics of linear and nonlinear optimization analysis for both single and multi-objective problems in hydrosystem engineering. The book includes several examples with various levels of complexity in different fields of water resources engineering. The examples are solved step by step to assist the reader and to make it easier to understand the concepts. In addition, the latest tools and methods are presented to help students, researchers, engineers and water managers to properly conceptualize and formulate resource allocation problems, and to deal with the complexity of constraints in water demand and available supplies in an appropriate way.
Download or read book Preparing Water Supply Systems for Climate Change written by Leslie DeCristofaro and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Fresh water is a resource strongly impacted by climactic conditions. Water supply systems in the northeastern United States will see the effects of climate change on their water quality and quantity in various ways, including changes in seasonality of flows, changes in the frequency and magnitude of extreme precipitation events, and changes in the variability of precipitation and water availability. Five northeastern water supplies examined are expected to maintain at least 95% monthly reliability over a range of climates wider than the current projections. However, model results indicate that turbidity levels in New York City's Ashokan Reservoir will change with changes in mean annual precipitation and temperature. Through a series of linked models of stochastic weather, hydrologic processes, and the supply system, Chapter 2 demonstrates the robustness of several adaptations available to the New York City Water Supply System to mitigate drought and manage water quality under climate change projections through the end of the century. Results illustrate how reducing demand and managing storage and releases based on hydrologic forecasting reduce the frequency of drought warnings and emergencies and improve system reliability in all climate change scenarios investigated. Through operations that limit turbidity propagation through the system and improvements to the Catskill Aqueduct to lower the minimum flow under conditions with high turbidity, results demonstrate decreases lower turbidity loads and a reduction in emergency Alum use. These options demonstrate cumulative benefits when used in combination. Chapter 3 seeks to quantify the amount of water supply system performance improvement that can be expected from improved forecasting in managing drought conditions. Using existing forecasts for Lancaster, PA, synthetic forecasts with varying quality, and a system model of the Baltimore, MD water supply system, this chapter demonstrated a method for quantifying improved system performance as a function of improved forecast quality, finding improvements in system performance to be approximately linear over a large range of forecast quality. Chapter 4 tests a new method for the creation of statistical first-order autoregressive streamflow forecasts by conditioning the parameters and ensemble variance on a "hydrologic regime," defined in several different ways. National Weather Service seasonal outlooks for precipitation are used as categorical forecasts of precipitation. The forecasts are found to have small positive skill, and for two of three sites, this skill is enough to result in small gains in the CRPSS of the ensemble hydrologic forecast. Utilizing perfect categorical forecasts indicates that adjusting the ensemble variance (rather than the autoregressive parameter) based on forecasted precipitation is responsible for the majority of improvements in skill for this method. The method is limited by the difficulty of long lead-time precipitation forecasting.
Download or read book Uncertainty and Forecasting of Water Quality written by M.B. Beck and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the International Institute for Applied Systems Analysis began its study of water quality modeling and management in 1977, it has been interested in the relations between uncertainty and the problems of model calibration and prediction. The work has focused on the theme of modeling poorly defined environmental systems, a principal topic of the effort devoted to environmental quality control and management. Accounting for the effects of uncertainty was also of central concern to our two case studies of lake eutrophication management, one dealing with Lake Balaton in Hungary and the other with several Austrian lake systems. Thus, in November 1979 we held a meeting at Laxenburg to discuss recent method ological developments in addressing problems associated with uncertainty and forecasting of water quality. This book is based on the proceedings of that meeting. The last few years have seen an increase in awareness of the issue of uncertainty in water quality and ecological modeling. This book is relevant not only to contemporary issues but also to those of the future. A lack of field data will not always be the dominant problem for water quality modeling and management; more sophisticated measuring techniques and more comprehensive monitoring networks will come to be more widely applied. Rather, the important problems of the future are much more likely to emerge from the enhanced facility of data processing and to concern the meaningful interpretation, assimilation., and use of the information thus obtained.
Download or read book Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and published by MDPI. This book was released on 2019-02-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Download or read book Water Industry Systems written by Dragan Savic and published by . This book was released on 1999 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: These conference proceedings reflect the current and future roles of modeling and optimization in the description and management of water industry systems. Balanced views of academic and industry experts from around the world are included in the two volumes of papers. Insights are provided into the experiences of leading researchers and practitioners in applying modelling and optimization to the management of water quantity and quality. The topics covered are: advanced modelling techniques, risk management, process control and optimization, with particular emphasis on the development and implementation of emerging technologies. Application areas include both water supply and waste water disposal.
Download or read book Mainstreaming Multi mission Satellite Observations in Operational Water Resources Management written by Nishan Kumar Biswas and published by . This book was released on 2021 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective water management depends on the accuracy of three key components: monitoring, forecasting, and quantification of human impacts. Understanding complex hydrological processes and the vast amount of data are prerequisites for monitoring and prediction of water resources. Although ground-based measurement is the best way to monitor, it is impossible to measure all relevant geophysical variables at the required spatiotemporal scale. As a complementary source, satellite remote sensing has proven its application potential during the last decade. A single satellite's observation capability was further enhanced by using the compound eye view afforded by multiple satellites. A combination of multi-mission platforms, numerical modeling, and advances in computational resources facilitated the way of better water management. The overarching goal of this dissertation was to provide a proof-of-concept of mainstreaming the application of multi-satellite observation-based water management in data-limited regions.Among the three primary water management components, a compound-eye satellite-based monitoring method was developed to improve river height from the altimeter satellite in the second chapter. In the proposed method, river morphology information from ancillary satellites (Landsat and Sentinel 1-SAR) was extracted and applied to get the altimeter height based on derived morphology. The accuracy of the method was tested over river locations with diverse hydraulic characteristics. It was found that the river-morphology based method can improve the conventional altimeter height estimation method in dynamically changing rivers. The forecasting component of water management was studied by applying satellite observations and numerical weather prediction (NWP) model outputs in extreme event forecasting and presented in the third chapter. Nowcast and forecast meteorological parameters (without applying computationally expensive downscaling methods) and land-surface variables were forced in a hydrologic-hydrodynamic framework to generate skillful forecasts for up to 5 days. The method was globally scalable and economically feasible for developing nations. In the fourth chapter, the quantification of the human impacts component of water management was studied. A global reservoir monitoring framework was built to investigate the impact of existing and proposed dams. A satellite data-based mass balance approach was used to quantify reservoir outflow. In the last chapter, this framework was used to study the impact of existing reservoirs and to optimize the benefits of future dams/reservoirs. This tool helped the user community understand the global picture of how dams and reservoirs are impacting natural flow. The ability to quantify human impacts has broad implications on water management decisions. This dissertation promoted societal applications of satellites among water managers and policymakers through the four studies over three critical water management components. The greater transparency in water resources management and operations from this study allowed for more informed decisions regarding flood management and water supply security. The collection of completed works on water management showed how the vantage of space could “level the playing field” between nations and stakeholders competing for limited water resources, ultimately leading to greater cooperation.
Download or read book Review of the New York City Watershed Protection Program written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-04 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: New York City's municipal water supply system provides about 1 billion gallons of drinking water a day to over 8.5 million people in New York City and about 1 million people living in nearby Westchester, Putnam, Ulster, and Orange counties. The combined water supply system includes 19 reservoirs and three controlled lakes with a total storage capacity of approximately 580 billion gallons. The city's Watershed Protection Program is intended to maintain and enhance the high quality of these surface water sources. Review of the New York City Watershed Protection Program assesses the efficacy and future of New York City's watershed management activities. The report identifies program areas that may require future change or action, including continued efforts to address turbidity and responding to changes in reservoir water quality as a result of climate change.
Download or read book Anticipatory Water Management written by Schalk-jan Andel and published by CRC Press. This book was released on 2018-06-28 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have time to take appropriate action. This is called Anticipatory Water Management. Anticipatory actions may have adverse effects, such as when flood control actions turn out to have been unnecessary, because the actual rainfall was less than predicted. Therefore the uncertainty of the forecasts and the associated risks of applying Anticipatory Water Management have to be assessed. To facilitate this assessment, meteorological institutes are providing ensemble predictions to estimate the dynamic uncertainty of weather forecasts. This dissertation presents ways of improving the end-use of ensemble predictions in Anticipatory Water Management.
Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
Download or read book Next Generation Earth System Prediction written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2016-08-22 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.
Download or read book Completing the Forecast written by National Research Council and published by National Academies Press. This book was released on 2006-11-09 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
Download or read book Improving Hydrologic Prediction Via Data Assimilation Data Fusion and High resolution Modeling written by Arezoo Rafieei Nasab and published by . This book was released on 2017 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: With population growth, urbanization and climate change, accurate and skillful monitoring and prediction of water resources and water-related hazards are becoming increasingly important to maintaining and improving the quality of life for human beings and well-being of the ecosystem in which people live. Because most hydrologic systems are driven by atmospheric processes that are chaotic, hydrologic processes operate at many different scales, and the above systems are almost always under-observed, there are numerous sources of error in hydrologic prediction. This study aims to advance the understanding of these uncertainty sources and reduce the uncertainties to the greatest possible extent. Toward that end, we comparatively evaluate two data assimilation (DA) techniques ensemble Kalman filter (EnKF) and maximum likelihood ensemble filter (MLEF) to reduce the uncertainty in initial conditions of soil moisture. Results show MLEF is a strongly favorable technique for assimilating streamflow data for updating soil moisture. In most places, precipitation is by far the most important forcing in hydrologic prediction. Because radars do not measure precipitation directly, radar QPEs are subject to various sources of error. In this study, the three Next Generation Radar (NEXRAD)-based QPE products, the Digital Hybrid Scan Reflectivity (DHR), Multisensor Precipitation Estimator (MPE) and Next Generation Multisensor QPE (Q2), and the radar QPE from the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar are comparatively evaluated for high-resolution hydrologic modeling in the Dallas-Fort Worth Metroplex (DFW) area. Also, since they generally carry complementary information, one may expect to improve accuracy by fusing multiple QPEs. This study develops and comparatively evaluates four different techniques for producing high-resolution QPE by fusing multiple radar-based QPEs. Two experiments were carried out for evaluation; in one, the MPE and Q2 products were fused and, in the other, the MPE and CASA products were fused. Result show that the Simple Estimation (SE) is an effective, robust and computationally inexpensive data fusion algorithm for QPE. The other main goal of this study is to provide accurate spatial information of streamflow and soil moisture via distributed hydrologic modeling. Toward that end, we evaluated the NWS's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over the Trinity River Basin for several headwater basins. We also develop a prototype high resolution flash flood prediction system for Cities of Fort Worth, Arlington and Grand Prairie, a highly urbanized area. Ideally, the higher the resolution of distributed modeling and the precipitation input is, the more desirable the model output is as it provides better spatiotemporal specificity. There are, however, practical limits to the resolution of modeling. To test and ascertain the limits of high-resolution polarimetric QPE and distributed hydrologic modeling for advanced flash flood forecasting in large urban area, we performed sensitivity analysis to spatiotemporal resolution. The results indicate little consistent pattern in dependence on spatial resolution while there is a clear pattern for sensitivity to temporal resolution. More research is needed, however, to draw firmer conclusions and to assess dependence on catchment scale.
Download or read book Advancing Model Diagnostics to Support Hydrologic Prediction and Water Resources Planning Under Uncertainty written by Jonathan Drew Herman and published by . This book was released on 2015 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational models are essential tools for prediction and planning in water resources systems to ensure human water security and environmental health. Water systems models merely approximate the processes by which water moves through natural and built environments; their value depends on assumptions regarding climate, demand, land use, and other uncertain factors that may influence decision making. Numerical techniques to explore the role of these uncertain factors, known as diagnostic methods, can highlight opportunities to improve the accuracy of prediction as well as identify influential uncertainties to inform additional research and policy. This dissertation advances diagnostic methods for water resources models to identify (1) time-varying dominant processes driving modeled hydrologic predictions in flood forecasting, and (2) tradeoffs and vulnerabilities to changing climate and demands in regional urban water supply systems planning for drought. This work proposes diagnostic methods as a key element of a posteriori decision support, in which decision alternatives and vulnerable scenarios are identified following computational modeling and data analysis. Consistent with this theme, this work follows a multi-objective approach in which stakeholders can analyze tradeoffs between conflicting objectives as part of an iterative constructive learning process. For a spatially distributed flood forecasting model, results show that dominant uncertainties vary in space and time, and can inform model-based scientific inference as well as decision making. Similarly, the results of the urban water supply study indicate that sensitivity analysis can suggest costeffective paths to mitigate vulnerability to deeply uncertain future scenarios, for which likelihoods remain unknown or disputed. The multi-objective approach allows stakeholders to explore tradeoffs in their modeled robustness to inform intra-regional policies such as transfer contracts and shared infrastructure investments. Bridging the areas of hydrology and water systems planning is increasingly valuable, as hydrologic modelers begin to incorporate anthropogenic influences on the water cycle, and water systems planners begin to explore uncertainty in hydrologic process representation. In summary, this work develops diagnostic methods to identify time-varying dominant processes in distributed flood forecasting as well as tradeoffs and vulnerabilities under change in regional urban water supply, ultimately seeking to improve model-based planning for extreme floods and droughts in water resources systems.
