Science.gov

Sample records for advanced hydrologic prediction

  1. Advanced Hydrologic Prediction Services (AHPS) Science Infusion Strategy

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Smith, G.; Carter, G.

    2002-05-01

    NWS is implementing an Advanced Hydrologic Prediction Services (AHPS) Science initiative to meet NWS Vision 2005 goals and related hydrologic services requirements, including the goal of being a world leader using state of the art forecast science and technology. AHPS includes a science infusion strategy to meet the following objectives: extend forecast lead time, improve forecast accuracy, and provide better information for user decisions. AHPS will meet these goals by implementing hydrologic forecast models tuned to local conditions and operated to account for uncertainty in hydrologic forecasts. AHPS will use ensemble weather and climate forecasts of precipitation and other conditions, such as air temperature, that affect the forecasts. This ensemble approach to weather, climate and water forecasting will provide a probabilistic basis for AHPS forecast products. Meeting AHPS goals and objectives requires an infusion of new science into the existing forecast system. Three AHPS requirements for science infusion are: 1. Quantify the uncertainty of river forecasts and provide users with a clear view of future hydrologic conditions together with hard evidence that AHPS products are based on valid forecast probability information; 2. Reduce the space and time scale, improve the accuracy, and extend the lead time of hydrologic forecasts. Demonstrate that new improvements to hydrologic forecast procedures add value to the forecasts and meet user requirements; 3. Improve the ability of forecasters to use the tools provided by integrating these into an efficient operational forecast system that includes automatic techniques for data quality control, access to data, model calibration, data assimilation, processing of ensemble forecasts, verification of forecasts and monitoring of all stages of the forecast process.

  2. Hydrological Ensemble Prediction System (HEPS)

    NASA Astrophysics Data System (ADS)

    Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of ensembles for weather forecasting, the hydrological community now moves increasingly towards Hydrological Ensemble Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic Ensemble Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological ensemble processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and downscaling, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for downscaling and post-processing of weather ensembles to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and

  3. Advancing Ensemble Streamflow Prediction with Stochastic Meteorological Forcings for Hydrologic Modeling

    NASA Astrophysics Data System (ADS)

    Caraway, N.; Wood, A. W.; Rajagopalan, B.; Zagona, E. A.; Daugherty, L.

    2012-12-01

    River Forecast Centers of National Weather Service (NWS) produce seasonal streamflow forecasts via a method called Ensemble Streamflow Prediction (ESP). NWS ESP forces the temperature index Snow17 and Sacramento Soil Moisture Accounting model (SAC-SMA) models with historical weather sequences for the forecasting period, starting from models' current watershed initial conditions, to produce ensemble streamflow forecasts. There are two major drawbacks of this method: (i) the ensembles are limited to the length of historical, limiting ensemble variability and (ii) incorporating seasonal climate forecasts (e.g., El Nino Southern Oscillation) relies on adjustment or weighting of ESP streamflow sequences. These drawbacks motivate the research presented here, which has two components: (i) a multi-site stochastic weather generator and (ii) generation of ensemble weather forecast inputs to the NWS model to produce ensemble streamflow forecasts. We enhanced the K-nearest neighbor bootstrap based stochastic generator include: (i) clustering the forecast locations into climatologically homogeneous regions to better capture the spatial heterogeneity and, (ii) conditioning the weather forecasts on a probabilistic seasonal climate forecast. This multi-site stochastic weather generator runs in R and the NWS models run within the new Community Hydrologic Prediction System, a forecasting sequence we label WG-ESP. The WG-ESP framework was applied to generate ensemble forecasts of spring season (April-July) streamflow in the San Juan River Basin, one of the major tributaries of the Colorado River, for the period 1981-2010. The hydrologic model requires daily weather sequences at 66 locations in the basin. The enhanced daily weather generator sequences captured the distributional properties and spatial dependence of the climatological ESP, and also generated weather sequences consistent with conditioning on seasonal climate forecasts. Spring season ensemble forecast lead times from

  4. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.

    2012-12-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  5. Modular Curriculum for Hydrologic Advancement (MOCHA)

    NASA Astrophysics Data System (ADS)

    Kelleher, C.; Wagener, T.; Gooseff, M.; McGlynn, B.; Marshall, L.; Meixner, T.; McGuire, K.; Sharma, P.; Zuppe, S.; Pfeiffer, C.

    2008-12-01

    In-class hydrology education is typically strongly biased towards the instructor's background and overcoming this limitation is burdensome within the time-constraints academia. This problem is particularly true for academics in tenure-track positions when most of the material development must occur. To overcome this challenge and advance a broader perspective of hydrology education, we are in the process of establishing the Modular Curriculum for Hydrologic Advancement (MOCHA). The objective is to create an evolving core curriculum for hydrology education freely available to, developed, and reviewed by the worldwide hydrologic community. We seek to establish an online faculty learning community for hydrology education and a modular core curriculum based on modern pedagogical standards. The goal of this effort is to support hydrology faculty in educating hydrologists that can solve today's and tomorrow's interdisciplinary problems that go far beyond the traditional disciplinary biased hydrology education most of us have experienced.

  6. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    NASA Astrophysics Data System (ADS)

    Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena

    2015-04-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  7. Hydrologic Ensemble Prediction: Challenges and Opportunities

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Bradley, A.

    2005-12-01

    Ensemble forecast techniques are beginning to be used for hydrological prediction by operational hydrological services throughout the world. These techniques are attractive because they allow effects of a wide range of sources of uncertainty on hydrological forecasts to be accounted for. Not only does ensemble prediction in hydrology offer a general approach to probabilistic prediction, it offers a significant new approach to improve hydrological forecast accuracy as well. But, there are many scientific challenges that must be overcome to provide users with high quality hydrologic ensemble forecasts. A new international project the Hydrologic Ensemble Prediction Experiment (HEPEX) was started last year to organize the scientific community to meet these challenges. Its main objective is to bring the international hydrological community together with the meteorological community to demonstrate how to produce reliable hydrological ensemble for decisions for the benefit of public health and safety, the economy and the environment. Topics that will be addressed by the HEPEX scientific community include techniques for using weather and climate information in hydrologic prediction systems, new methods in hydrologic prediction, data assimilation issues in hydrology and hydrometeorology, verification and correction of ensemble weather and hydrologic forecasts, and better quantification of uncertainty in hydrological prediction. As pathway for addressing these topics, HEPEX will set up demonstration test bed projects and compile data sets for the intercomparison of coupled systems for atmospheric and hydrologic forecasting, and their assessment for meeting end users' needs for decision-making. Test bed projects have been proposed in North and South America, Europe, and Asia, and have a focus ranging from short-range flood forecasting to seasonal predictions for water supply. For example, within the United States, ongoing activities in seasonal prediction as part of the GEWEX

  8. Hydrologic Prediction Through Earthcube Enabled Hydrogeophysical Cyberinfrastructure

    NASA Astrophysics Data System (ADS)

    Versteeg, R. J.; Johnson, D.

    2012-12-01

    Accurate prediction of hydrologic processes is contingent on the successful interaction of multiple components, including (1) accurate conceptual and numerical models describing physical, chemical and biological processes (2) a numerical framework for integration of such processes and (3) multidisciplinary temporal data streams which feeds such models. Over the past ten years the main focus in the hydrogeophysical community has been the advancement and developments of conceptual and numerical models. While this advancement still poses numerous challenges (e.g. the in silico modeling of microbiological processes and the coupling of models across different interfaces) there is now a fairly good high level understanding of the types, scales of and interplay between processes. In parallel with this advancement there have been rapid developments in data acquisition capabilities (ranging from satellite based remote sensing to low cost sensor networks) and the associated cyberinfrastructure which allows for mash ups of data from heterogeneous and independent sensor networks. The tools for this in generally have come from outside the hydrogeophysical community - partly these are specific scientific tools developed through NSF, DOE and NASA funding, and partly these are general web2.0 tools or tools developed under commercial initiatives (e.g. the IBM Smarter Planet initiative). One challenge facing the hydrogeophysical community is how to effectively harness all these tools to develop hydrologic prediction tools. One of the primary opportunities for this is the NSF funded EarthCube effort (http://earthcube.ning.com/ ). The goal of EarthCube is to transform the conduct of research by supporting the development of community-guided cyberinfrastructure to integrate data and information for knowledge management across the Geosciences. Note that Earthcube is part of a larger NSF effort (Cyberinfrastructure for the 21st Century (CIF21), and that Earthcube is driven by the vision

  9. The Modular Curriculum for Hydrologic Advancement (MOCHA)

    NASA Astrophysics Data System (ADS)

    Wagener, T.; Kelleher, C.; Gooseff, M.; McGlynn, B.; Marshall, L.; Meixner, T.; McGuire, K.; Sharma, P.; Zappe, S.

    2009-04-01

    In-class hydrology education is typically strongly biased towards the instructor's background and overcoming this limitation is overly burdensome within the time-constraints of the academic life. This is particularly true for academics in tenure-track positions when most of the material development has to occur. To overcome this issue, we are in the process of establishing the Modular Curriculum for Hydrologic Advancement (MOCHA). Our overall objective is to create an evolving core curriculum for hydrology education freely available to and developed and reviewed by the worldwide hydrologic community. We seek to establish an online faculty learning community for hydrology education and a modular core curriculum based on modern pedagogical standards. The goal of this effort is to support hydrology faculty in educating hydrologists that can solve today's and tomorrow's interdisciplinary problems that go far beyond the traditional disciplinary biased hydrology education most of us have experienced.

  10. Soil hydrology: Recent methodological advances, challenges, and perspectives

    NASA Astrophysics Data System (ADS)

    Vereecken, H.; Huisman, J. A.; Hendricks Franssen, H. J.; Brüggemann, N.; Bogena, H. R.; Kollet, S.; Javaux, M.; van der Kruk, J.; Vanderborght, J.

    2015-04-01

    Technological and methodological progress is essential to improve our understanding of fundamental processes in natural and engineering sciences. In this paper, we will address the potential of new technological and methodological advancements in soil hydrology to move forward our understanding of soil water related processes across a broad range of scales. We will focus on advancements made in quantifying root water uptake processes, subsurface lateral flow, and deep drainage at the field and catchment scale, respectively. We will elaborate on the value of establishing a science-driven network of hydrological observatories to test fundamental hypotheses, to study organizational principles of soil hydrologic processes at catchment scale, and to provide data for the development and validation of models. Finally, we discuss recent developments in data assimilation methods, which provide new opportunities to better integrate observations and models and to improve predictions of the short-term evolution of hydrological processes.

  11. Hydrologic ensemble prediction experiment focuses on reliable forecasts

    NASA Astrophysics Data System (ADS)

    Franz, Kristie; Ajami, Newsha; Schaake, John; Buizza, Roberto

    The Hydrologic Ensemble Prediction Experiment (HEPEX), an effort involving meteorological and hydrological scientists from research, operational, and user communities around the globe, is building a research project focused on advancing probabilistic hydrologic forecasting.HEPEX was launched in March 2004 at a meeting hosted by the European Centre for Medium-Range Weather Forecasts (ECMWF), in Reading, United Kingdom http://www.ecmwf.int/newsevents/meetings/workshops/2004/HEPEX/). The goal of HEPEX is “to bring the international hydrological and meteorological communities together to demonstrate how to produce reliable hydrological ensemble forecasts that can be used with confidence by the emergency management and water resources sectors to make decisions that have important consequences for the economy, public health, and safety.”

  12. A University Consortium for the Advancement of Hydrologic Research

    NASA Astrophysics Data System (ADS)

    Hooper, R. P.; Wilson, J.; Band, L.; Reckhow, K.

    2003-12-01

    Seventy-six research universities across the United States have joined to form the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), a non-profit corporation. With support from the National Science Foundation, CUAHSI has embarked upon the design and development of programs to enable hydrologic research at larger spatial scales over longer time periods than has been within the grasp of individual investigators. The guiding principle of this design has been an embracing of the entire hydrologic cycle to enable research at the interfaces among traditional hydrologic subdisciplines and between hydrologic science and allied disciplines in the earth and life sciences. To improve our predictive understanding of hydrologic phenomena, the fundamental approach that has been adopted is the development of multidisciplinary, coherent data sets to enable testing of hypotheses in different hydrologic settings across a range of spatial and temporal scales. Four mutually supportive program elements have been conceived: a network of hydrologic observatories (the subject of this special session) designed strategically to collect additional data at large scales (on the order of 10,000 km2) and to leverage existing investments in small-scale intensive studies and in larger scale monitoring activities; hydrologic information systems to develop a comprehensive data model for integrating disparate data types, to develop the cyberinfrastructure necessary for systematic data collection and dissemination and to support community models; hydrologic measurement technology facility to broker instrumentation services from existing sources, to provide cutting edge tools along with the necessary support to use them, and to develop new hydrologic instrumentation needed to advance the science; and hydrologic synthesis center to provide a venue for hydrologic sciences from a range of disciplines to work on topics ranging from inter-observatory comparison to evolving

  13. Accelerating advances in continental domain hydrologic modeling

    NASA Astrophysics Data System (ADS)

    Archfield, Stacey A.; Clark, Martyn; Arheimer, Berit; Hay, Lauren E.; McMillan, Hilary; Kiang, Julie E.; Seibert, Jan; Hakala, Kirsti; Bock, Andrew; Wagener, Thorsten; Farmer, William H.; Andréassian, Vazken; Attinger, Sabine; Viglione, Alberto; Knight, Rodney; Markstrom, Steven; Over, Thomas

    2015-12-01

    In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.

  14. It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA)

    NASA Astrophysics Data System (ADS)

    Wagener, T.; Kelleher, C.; Weiler, M.; McGlynn, B.; Gooseff, M.; Marshall, L.; Meixner, T.; McGuire, K.; Gregg, S.; Sharma, P.; Zappe, S.

    2012-02-01

    Protection from hydrological extremes and the sustainable supply of hydrological services in the presence of climate change and increasing population pressure are the defining societal challenges for hydrology in the 21st century. A review of the existing literature shows that these challenges and their educational consequences for hydrology were foreseeable and were predicted by some. Surveys of the current educational basis, however, also clearly demonstrate that hydrology education is not yet prepared to deal with this challenge. We present our own vision of the necessary future evolution of hydrology education, which we implemented in the Modular Curriculum for Hydrologic Advancement (MOCHA). The MOCHA project is directly aimed at developing a community-driven basis for hydrology education. In this paper we combine literature review, surveys, discussion and assessment to provide a holistic baseline for future hydrology education.

  15. Using climate model ensemble forecasts for seasonal hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Wood, Andrew Whitaker

    Seasonal hydrologic forecasting has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate seasonal climate forecast information that has become operationally available during the last decade. This study is motivated by the view that a combination of hydrologic and climate prediction methods affords a new opportunity to improve hydrologic forecast skill. A relatively direct statistical approach for achieving this combination (i.e., downscaling) was formulated that used ensemble climate model forecasts with a six month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as input to the macroscale Variable Infiltration Capacity hydrologic model to produce ensemble runoff and streamflow forecasts. The approach involved the bias correction of climate model precipitation and temperature fields, and spatial and temporal disaggregation from monthly climate model scale (about 2 degrees latitude by longitude) fields to daily hydrology model scale (1/8 degrees) inputs. A qualitative evaluation of the approach in the eastern U.S. suggested that it was successful in translating climate forecast signals to local hydrologic variables and streamflow, but that the dominant influence on forecast results tended to be persistence in initial hydrologic conditions. The suitability of the statistical downscaling approach for supporting hydrologic simulation was then assessed (using a continuous retrospective 20-year climate simulation from the DOE Parallel Climate Model) relative to dynamical downscaling via a regional, meso-scale climate model. The statistical approach generally outperformed the dynamical approach, in that the dynamical approach alone required additional bias-correction to reproduce the retrospective hydrology as well as the statistical approach. Finally, using 21 years of

  16. Effect of Rainfall Aggregation on Hydrologic Predictions

    NASA Astrophysics Data System (ADS)

    Sharif, H.; Brandes, E.

    2003-12-01

    Remotely sensed soil moisture data are becoming increasingly available, however the variability within the remotely sensed footprint is spatially averaged. The representation of spatial heterogeneity of soil moisture is essential for modeling processes that are nonlinearly related to soil moisture, such as the partitioning of sensible and latent heat fluxes. A number of studies have suggested that the spatial variability of soil moisture varies with wetness. At different locations, scales, and wetting and drying conditions, soil moisture patterns have been linked to topography, soil characteristics such as porosity and wilting point, and rainfall distribution. The objective of the proposed study is to examine the effects of rainfall temporal and spatial aggregation on spatial variability of soil moisture and runoff predictions on a 1000-km2 watershed. High-resolution radar-estimated rainfall from the IHOP2002 experiment will be used. These rain fields are aggregated in space and time. The hydrologic response of a distributed hydrologic model to the aggregated rain fields will be statistically compared with the response of the model to the original rainfall fields to quantify the impact of the spatial and temporal aggregation on hydrologic predictions. The proposed procedure will combine information from these simulations to determine what adjustments need to be made to the predicted fluxes.

  17. Recent advances in understanding Antarctic subglacial lakes and hydrology.

    PubMed

    Siegert, Martin J; Ross, Neil; Le Brocq, Anne M

    2016-01-28

    It is now well documented that over 400 subglacial lakes exist across the bed of the Antarctic Ice Sheet. They comprise a variety of sizes and volumes (from the approx. 250 km long Lake Vostok to bodies of water less than 1 km in length), relate to a number of discrete topographic settings (from those contained within valleys to lakes that reside in broad flat terrain) and exhibit a range of dynamic behaviours (from 'active' lakes that periodically outburst some or all of their water to those isolated hydrologically for millions of years). Here we critique recent advances in our understanding of subglacial lakes, in particular since the last inventory in 2012. We show that within 3 years our knowledge of the hydrological processes at the ice-sheet base has advanced considerably. We describe evidence for further 'active' subglacial lakes, based on satellite observation of ice-surface changes, and discuss why detection of many 'active' lakes is not resolved in traditional radio-echo sounding methods. We go on to review evidence for large-scale subglacial water flow in Antarctica, including the discovery of ancient channels developed by former hydrological processes. We end by predicting areas where future discoveries may be possible, including the detection, measurement and significance of groundwater (i.e. water held beneath the ice-bed interface). PMID:26667914

  18. Recent advances in understanding Antarctic subglacial lakes and hydrology

    PubMed Central

    Siegert, Martin J.; Ross, Neil; Le Brocq, Anne M.

    2016-01-01

    It is now well documented that over 400 subglacial lakes exist across the bed of the Antarctic Ice Sheet. They comprise a variety of sizes and volumes (from the approx. 250 km long Lake Vostok to bodies of water less than 1 km in length), relate to a number of discrete topographic settings (from those contained within valleys to lakes that reside in broad flat terrain) and exhibit a range of dynamic behaviours (from ‘active’ lakes that periodically outburst some or all of their water to those isolated hydrologically for millions of years). Here we critique recent advances in our understanding of subglacial lakes, in particular since the last inventory in 2012. We show that within 3 years our knowledge of the hydrological processes at the ice-sheet base has advanced considerably. We describe evidence for further ‘active’ subglacial lakes, based on satellite observation of ice-surface changes, and discuss why detection of many ‘active’ lakes is not resolved in traditional radio-echo sounding methods. We go on to review evidence for large-scale subglacial water flow in Antarctica, including the discovery of ancient channels developed by former hydrological processes. We end by predicting areas where future discoveries may be possible, including the detection, measurement and significance of groundwater (i.e. water held beneath the ice-bed interface). PMID:26667914

  19. Geostatistical enhancement of european hydrological predictions

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

    2016-04-01

    Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a

  20. Hydrologic Predictions in the Anthropocene: A Research Framework Based on a Co-evolutionary Socio-hydrologic Perspective

    NASA Astrophysics Data System (ADS)

    Sivapalan, M.; Bloeschl, G.

    2012-12-01

    The world is facing a water management crisis, in the context of fast rising demand for water due to growth of human populations and changing lifestyles, and depletion of freshwater resources. In many parts of the world, poor distribution of freshwater in relation to demand is already the cause of serious water scarcity, exacerbated by climate change. Cumulatively, these result in increased human appropriation of water resources, significant modification of landscapes, and a strong human imprint on water cycle dynamics from local to global scales. Hydrologic predictions in such a fast changing environment face significant challenges. Traditional models for predictions treat the hydrologic system as a simple input-output system, and propagate variability of external inputs or disturbances through the various hydrologic subsystems, but assuming stationarity. However, in a fast changing world, none of the subsystems can be assumed to be stationary, but as co-evolving parts of a complex system. The role of humans takes on an important role, which can no longer be assumed to independent of the natural system. We need new socio-hydrologic frameworks to observe, monitor, understand and predict the co-evolution of coupled human-natural systems. In this talk, using examples from one or more real-world settings (from Australia and Europe) involving human interactions with hydrologic systems, we will present new theoretical frameworks that should be adopted to advance the emergent new sub-discipline of socio-hydrology. The proposed research agenda is organized under (i) process socio-hydrology, (ii) comparative socio-hydrology, and (iii) historical socio-hydrology.

  1. A Hydrological Perspective to Advance Understanding of the Water Cycle

    NASA Astrophysics Data System (ADS)

    Berghuijs, W.

    2014-12-01

    In principle hydrologists are scientists that study relationships within the water cycle. Yet, current technology makes it tempting for hydrology students to lose their "hydrological perspective" and become instead full-time computer programmers or statisticians. I assert that students should ensure their hydrological perspective thrives, notwithstanding the importance and possibilities of current technology. This perspective is necessary to advance the science of hydrology. As other hydrologists have pondered similar views before, I make no claims of originality here. I just hope that in presenting my perspective on this issue I may spark the interest of other early career hydrologists.

  2. Acting, predicting and intervening in a socio-hydrological world

    NASA Astrophysics Data System (ADS)

    Lane, S. N.

    2014-03-01

    This paper asks a simple question: if humans and their actions co-evolve with hydrological systems (Sivapalan et al., 2012), what is the role of hydrological scientists, who are also humans, within this system? To put it more directly, as traditionally there is a supposed separation of scientists and society, can we maintain this separation as socio-hydrologists studying a socio-hydrological world? This paper argues that we cannot, using four linked sections. The first section draws directly upon the concern of science-technology studies to make a case to the (socio-hydrological) community that we need to be sensitive to constructivist accounts of science in general and socio-hydrology in particular. I review three positions taken by such accounts and apply them to hydrological science, supported with specific examples: (a) the ways in which scientific activities frame socio-hydrological research, such that at least some of the knowledge that we obtain is constructed by precisely what we do; (b) the need to attend to how socio-hydrological knowledge is used in decision-making, as evidence suggests that hydrological knowledge does not flow simply from science into policy; and (c) the observation that those who do not normally label themselves as socio-hydrologists may actually have a profound knowledge of socio-hydrology. The second section provides an empirical basis for considering these three issues by detailing the history of the practice of roughness parameterisation, using parameters like Manning's n, in hydrological and hydraulic models for flood inundation mapping. This history sustains the third section that is a more general consideration of one type of socio-hydrological practice: predictive modelling. I show that as part of a socio-hydrological analysis, hydrological prediction needs to be thought through much more carefully: not only because hydrological prediction exists to help inform decisions that are made about water management; but also because

  3. It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA)

    NASA Astrophysics Data System (ADS)

    Wagener, T.; Kelleher, C.; Weiler, M.; McGlynn, B.; Gooseff, M.; Marshall, L.; Meixner, T.; McGuire, K.; Gregg, S.; Sharma, P.; Zappe, S.

    2012-09-01

    Protection from hydrological extremes and the sustainable supply of hydrological services in the presence of changing climate and lifestyles as well as rocketing population pressure in many parts of the world are the defining societal challenges for hydrology in the 21st century. A review of the existing literature shows that these challenges and their educational consequences for hydrology were foreseeable and were even predicted by some. However, surveys of the current educational basis for hydrology also clearly demonstrate that hydrology education is not yet ready to prepare students to deal with these challenges. We present our own vision of the necessary evolution of hydrology education, which we implemented in the Modular Curriculum for Hydrologic Advancement (MOCHA). The MOCHA project is directly aimed at developing a community-driven basis for hydrology education. In this paper we combine literature review, community survey, discussion and assessment to provide a holistic baseline for the future of hydrology education. The ultimate objective of our educational initiative is to enable educators to train a new generation of "renaissance hydrologists," who can master the holistic nature of our field and of the problems we encounter.

  4. Gradation of complexity and predictability of hydrological processes

    NASA Astrophysics Data System (ADS)

    Sang, Yan-Fang; Singh, Vijay P.; Wen, Jun; Liu, Changming

    2015-06-01

    Quantification of the complexity and predictability of hydrological systems is important for evaluating the impact of climate change on hydrological processes, and for guiding water activities. In the literature, the focus seems to have been on describing the complexity of spatiotemporal distribution of hydrological variables, but little attention has been paid to the study of complexity gradation, because the degree of absolute complexity of hydrological systems cannot be objectively evaluated. Here we show that complexity and predictability of hydrological processes can be graded into three ranks (low, middle, and high). The gradation is based on the difference in the energy distribution of hydrological series and that of white noise under multitemporal scales. It reflects different energy concentration levels and contents of deterministic components of the hydrological series in the three ranks. Higher energy concentration level reflects lower complexity and higher predictability, but scattered energy distribution being similar to white noise has the highest complexity and is almost unpredictable. We conclude that the three ranks (low, middle, and high) approximately correspond to deterministic, stochastic, and random hydrological systems, respectively. The result of complexity gradation can guide hydrological observations and modeling, and identification of similarity patterns among different hydrological systems.

  5. Challenges and Opportunities for Hydrology Education in a Changing World - The Modular Curriculum for Hydrologic Advancement

    NASA Astrophysics Data System (ADS)

    McGlynn, Brian; Wagener, Thorsten; Marshall, Lucy; McGuire, Kevin; Meixner, Thomas; Weiler, Markus; Gooseff, Michael; Kelleher, Christa; Gregg, Susan

    2010-05-01

    ‘It takes a village to raise a child', but who does it take to educate a hydrologist who can solve today's and tomorrow's problems? Hydrology is inherently an interdisciplinary science, and therefore requires interdisciplinary training. We believe that the demands on current and future hydrologists will continue to increase, while training at undergraduate and graduate levels has not kept pace. How do we, as university faculty, educate hydrologists capable of solving complex problems in an interdisciplinary environment considering that current educators have often been taught in narrow traditional disciplines? We suggest a unified community effort to change the way that hydrologists are educated. The complexity of the task is ever increasing. Analysis techniques and tools required for solving emerging problems have to evolve away from focusing mainly on the analysis of past behavior because baselines are shifting as the world changes. The difficulties of providing an appropriate education are also increasing, especially given the growing demands on faculty time. To support hydrology educators and improve hydrology education, we have started a faculty community of educators (REACH) and implemented the Modular Curriculum for Hydrologic Advancement (MOCHA, http://www.mocha.psu.edu/). The goal of this effort is to support hydrology faculty as they educate hydrologists that can solve interdisciplinary problems that go far beyond the traditional disciplinary biased hydrology education most of us have experienced as students. Our current objective is to create an evolving core curriculum for university hydrology education, based on modern pedagogical standards, freely available to and developed and reviewed by the worldwide hydrologic community. We seek to establish an online faculty learning community for hydrology education and capacity building. In this presentation we discuss the results of a recent survey on current hydrology education (to compare with the state of

  6. Predicting Epileptic Seizures in Advance

    PubMed Central

    Moghim, Negin; Corne, David W.

    2014-01-01

    Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance. PMID:24911316

  7. Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

    NASA Technical Reports Server (NTRS)

    Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.; van Velzen, N.; He, M.; Lee, H.; Noh, S. J.; Rakovec, O.; Restrepo, P.

    2012-01-01

    Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA

  8. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Weerts, A. H.; Clark, M.; Hendricks Franssen, H.-J.; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.; van Velzen, N.; He, M.; Lee, H.; Noh, S. J.; Rakovec, O.; Restrepo, P.

    2012-03-01

    Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented into operational forecast systems to improve the skill of forecasts to better inform real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, The Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical considerations in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modelling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers

  9. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Weerts, A. H.; Clark, M.; Hendricks Franssen, H.-J.; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.; van Velzen, N.; He, M.; Lee, H.; Noh, S. J.; Rakovec, O.; Restrepo, P.

    2012-10-01

    Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA

  10. Acting, predicting and intervening in a socio-hydrological world

    NASA Astrophysics Data System (ADS)

    Lane, S. N.

    2013-08-01

    This paper asks a simple question: if humans and their actions co-evolve with hydrological systems (Sivapalan and Blöschl, 2012), what is the role of those humans who are simultaneously hydrological scientists, bound up within this system? To put it more directly, can we, as socio-hydrologists study the socio-hydrological world in isolation from that world in a way that mirrors the supposed separation between scientists and society? I answer this question, in the negative, from three linked perspectives. The first draws directly upon science-technology studies to make a case to the (socio-hydrological) community that we need to be sensitive to constructivist accounts of science in general and hydrology in particular. I review three positions taken by such accounts and apply them to hydrological science, supported with specific examples: (a) the philosophical critique of the claimed abstraction of scientists and scientific activity from the socio-hydrological world; (b) the way in which hydrological science is embedded in wider societal decision-making; and (c) the recognition that socio-hydrological knowledge is much more distributed than we as (socio-)hydrologists commonly recognise. For the second perspective, I consider predictive modelling as a socio-hydrological practice. I draw upon wider studies of the practice of modelling, coupled to empirical evidence for one element of hydrological modelling, roughness parameterisation, to consider how it is that socio-hydrological modellers come to believe in the predictive models that they use. This will show that if predictive modelling is to be more than analytical, that if it is to effect more sustainable socio-hydrological futures, then we need to rethink the basic tenets of how we practice predictive modelling. These first two perspectives are themselves, in combination, analytical, prone to the criticism that they cause us to degenerate into an "anything goes" relationship with the world around us. Thus, in a

  11. Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics

    NASA Astrophysics Data System (ADS)

    Brooks, Paul D.; Chorover, Jon; Fan, Ying; Godsey, Sarah E.; Maxwell, Reed M.; McNamara, James P.; Tague, Christina

    2015-09-01

    Hydrology is an integrative discipline linking the broad array of water-related research with physical, ecological, and social sciences. The increasing breadth of hydrological research, often where subdisciplines of hydrology partner with related sciences, reflects the central importance of water to environmental science, while highlighting the fractured nature of the discipline itself. This lack of coordination among hydrologic subdisciplines has hindered the development of hydrologic theory and integrated models capable of predicting hydrologic partitioning across time and space. The recent development of the concept of the critical zone (CZ), an open system extending from the top of the canopy to the base of groundwater, brings together multiple hydrological subdisciplines with related physical and ecological sciences. Observations obtained by CZ researchers provide a diverse range of complementary process and structural data to evaluate both conceptual and numerical models. Consequently, a cross-site focus on "critical zone hydrology" has potential to advance the discipline of hydrology and to facilitate the transition of CZ observatories into a research network with immediate societal relevance. Here we review recent work in catchment hydrology and hydrochemistry, hydrogeology, and ecohydrology that highlights a common knowledge gap in how precipitation is partitioned in the critical zone: "how is the amount, routing, and residence time of water in the subsurface related to the biogeophysical structure of the CZ?" Addressing this question will require coordination among hydrologic subdisciplines and interfacing sciences, and catalyze rapid progress in understanding current CZ structure and predicting how climate and land cover changes will affect hydrologic partitioning.

  12. CUAHSI's Hydrologic Measurement Facility: Putting Advanced Tools in Scientists' Hands

    NASA Astrophysics Data System (ADS)

    Hooper, R. P.; Robinson, D.; Selker, J.; Duncan, J.

    2006-05-01

    Like related environmental sciences, the hydrologic sciences community has been defining environmental observatories and the support components necessary for their successful implementation, such as informatics (cyberinfrastructure) and instrumentation. Unlike programs, such as NEON and OOI, that have been pursuing large-scale capital funding through the Major Research Equipment program of the National Science Foundation, CUAHSI has been pursuing incremental development of observatories that has allowed us to pilot different parts of these support functions, namely Hydrologic Information Systems and a Hydrologic Measurement Facility (HMF), the subject of this paper. The approach has allowed us to gain greater specificity of the requirements for these facilities and their operational challenges. The HMF is developing the foundation to support innovative research across the breadth of the Hydrologic Community, including classic PI-driven projects as well as over 20 grass-roots observatories that have been developing over the past 2 years. HMF is organized around three basic areas: water cycle instrumentation, biogeochemistry and geophysics. Committees have been meeting to determined the most effective manner to deliver instrumentation, whether by special instrumentation packages proposed by host institutions; collaborative agreements with federal agencies; and contributions from industrial partners. These efforts are guided by the results of a community wide survey conducted in Nov-Dec 2005, and a series of ongoing workshops. The survey helped identify the types of equipment that will advance hydrological sciences and are often beyond the capabilities of individual PI's. Respondents to the survey indicated they were keen for HMF to focus on providing supported equipment such as atmospheric profilers like LIDAR, geophysical instrumentation ranging from airborne sensors to ground-penetrating radar, and field-deployed mass spectrophotometers. A recently signed agreement

  13. Comparing Sediment Yield Predictions from Different Hydrologic Modeling Schemes

    NASA Astrophysics Data System (ADS)

    Dahl, T. A.; Kendall, A. D.; Hyndman, D. W.

    2015-12-01

    Sediment yield, or the delivery of sediment from the landscape to a river, is a difficult process to accurately model. It is primarily a function of hydrology and climate, but influenced by landcover and the underlying soils. These additional factors make it much more difficult to accurately model than water flow alone. It is not intuitive what impact different hydrologic modeling schemes may have on the prediction of sediment yield. Here, two implementations of the Modified Universal Soil Loss Equation (MUSLE) are compared to examine the effects of hydrologic model choice. Both the Soil and Water Assessment Tool (SWAT) and the Landscape Hydrology Model (LHM) utilize the MUSLE for calculating sediment yield. SWAT is a lumped parameter hydrologic model developed by the USDA, which is commonly used for predicting sediment yield. LHM is a fully distributed hydrologic model developed primarily for integrated surface and groundwater studies at the watershed to regional scale. SWAT and LHM models were developed and tested for two large, adjacent watersheds in the Great Lakes region; the Maumee River and the St. Joseph River. The models were run using a variety of single model and ensemble downscaled climate change scenarios from the Coupled Model Intercomparison Project 5 (CMIP5). The initial results of this comparison are discussed here.

  14. Advances in borehole geophysics for hydrology

    SciTech Connect

    Nelson, P.H.

    1982-01-01

    Borehole geophysical methods provide vital subsurface information on rock properties, fluid movement, and the condition of engineered borehole structures. Within the first category, salient advances include the continuing improvement of the borehole televiewer, refinement of the electrical conductivity dipmeter for fracture characterization, and the development of a gigahertz-frequency electromagnetic propagation tool for water saturation measurements. The exploration of the rock mass between boreholes remains a challenging problem with high potential; promising methods are now incorporating high-density spatial sampling and sophisticated data processing. Flow-rate measurement methods appear adequate for all but low-flow situations. At low rates the tagging method seems the most attractive. The current exploitation of neutron-activation techniques for tagging means that the wellbore fluid itself is tagged, thereby eliminating the mixing of an alien fluid into the wellbore. Another method uses the acoustic noise generated by flow through constrictions and in and behind casing to detect and locate flaws in the production system. With the advent of field-recorded digital data, the interpretation of logs from sedimentary sequences is now reaching a sophisticated level with the aid of computer processing and the application of statistical methods. Lagging behind are interpretive schemes for the low-porosity, fracture-controlled igneous and metamorphic rocks encountered in the geothermal reservoirs and in potential waste-storage sites. Progress is being made on the general problem of fracture detection by use of electrical and acoustical techniques, but the reliable definition of permeability continues to be an elusive goal.

  15. Hydrologic predictions on ungauged catchments using deterministic distributed modelling system

    NASA Astrophysics Data System (ADS)

    Tachecí, Pavel; Kimlová, Martina

    2010-05-01

    There is a need for warning system giving prediction of flash-flood risk conditions with sufficient advance even in source areas and in small tributaries catchments. New approach is based on combination of numerical weather prediction (NWP) model, radar or rain gauge data with distributed hydrologic mathematical model of particular area. Set of newly developed tools, customized for particular use in the Czech Hydrometeorological Institute (CHMI) environment enhance import of data and presentation of results. This forecast system focuses on hydrological modelling of running water balance in spatially distributed manner. Its computation is repeated day-to-day. Six models of particular basins (800 - 4000 km2), representing different conditions across the Czech Republic territory were calibrated and validated successfully. The Sázava river basin model (4.000 km2) is used for regular testing operation in CHMI Forecast centre since October 2007. Basic size of grid cells used in models is 300x300 m, basic time step of forecast is 1 day, but can be refined according to the input data. Water balance is computed using simplified 2-layer method for unsaturated zone, 2D approximation of Boussinesq equation for saturated zone, diffusion equation for overland flow and 1D kinematic equation for river flow (MIKE 11 model). The whole process of input data processing, model simulation and result generation may be run automatically or in step-by step mode via simple graphical user interface. Three types of input data are supported: •time series (temperature and precipitation) measured at observation stations and stored in CHMI database •radar data products (precipitation intensity field) •results of ALADIN weather forecast model (temperature and precipitation field). For forecast purposes, reference evapotranspiration is approximated according relationship to air temperature for every computational grid cell. The user may choose area (catchment) to be processed and period of

  16. Hydrology

    NASA Astrophysics Data System (ADS)

    Brutsaert, Wilfried

    2005-08-01

    Water in its different forms has always been a source of wonder, curiosity and practical concern for humans everywhere. Hydrology - An Introduction presents a coherent introduction to the fundamental principles of hydrology, based on the course that Wilfried Brutsaert has taught at Cornell University for the last thirty years. Hydrologic phenomena are dealt with at spatial and temporal scales at which they occur in nature. The physics and mathematics necessary to describe these phenomena are introduced and developed, and readers will require a working knowledge of calculus and basic fluid mechanics. The book will be invaluable as a textbook for entry-level courses in hydrology directed at advanced seniors and graduate students in physical science and engineering. In addition, the book will be more broadly of interest to professional scientists and engineers in hydrology, environmental science, meteorology, agronomy, geology, climatology, oceanology, glaciology and other earth sciences. Emphasis on fundamentals Clarification of the underlying physical processes Applications of fluid mechanics in the natural environment

  17. Numerical prediction of subsidence with coupled geomechanical-hydrological modeling

    SciTech Connect

    Girrens, S.P.; Anderson, C.A.; Bennett, J.G.; Kramer, M.

    1981-01-01

    A coupled finite element geomechanical-hydrology code is currently under development for application to the problem of predicting groundwater disturbances associated with mine subsidence. The structural-fluid coupling is addressed by calculating the subsided mine geometry, with emphasis placed on determining the strata disturbance and locating damaged regions, for input into a hydrology code, which determines localized volume flow rates and aquifer fluctuations. Benefits from coupling will be best realized when field measurements, an additional aspect of the study concurrent with analytical investigations, indicating the relationship between increasing rock strain and increasing permeability are incorporated into hydraulic material descriptions. Hydrologic and structural calculations are presented to demonstrate computational capabilities applicable to mine subsidence.

  18. Ensemble stream flow predictions, a way towards better hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Edlund, C.

    2009-04-01

    The hydrological forecasting division at SMHI has been using hydrological EPS and hydrological probabilities forecasts operationally since some years ago. The inputs to the hydrological model HBV are the EPS forecasts from ECMWF. From the ensemble, non-exceedance probabilities are estimated and final correction of the ensemble spread, based on evaluation is done. Ensemble stream flow predictions are done for about 80 indicator basins in Sweden, where there is a real-time discharge gauge. The EPS runs are updated daily against the latest observed discharge. Flood probability maps for exceeding a certain threshold, i.e. a certain warning level, are produced automatically once a day. The flood probabilistic forecasts are based on a HBV- model application, (called HBV-Sv, HBV Sweden) that covers the whole country and consist of 1001 subbasins with an average size between 200 and 700 km2. Probabilities computations for exceeding a certain warning level are made for each one of these 1001 subbasins. Statistical flood levels have been calculated for each river sub-basin. Hydrological probability forecasts should be seen as an early warning product that can give better support in decision making to end-users communities, for instance Civil Protections Offices and County Administrative Boards, within flood risk management. The main limitations with probability forecasts are: on one hand, difficulties to catch small-scale rain (mainly due to resolution of meteorological models); on the other hand, the hydrological model can't be updated against observations in all subbasins. The benefits of working with probabilities consist, first of all, of a new approach when working with flood risk management and scenarios. A probability forecast can give an early indication for Civil Protection that "something is going to happen" and to gain time in preparing aid operations. The ensemble stream flow prediction at SMHI is integrated with the national forecasting system and the products

  19. Remote sensing of vegetation ecophysiological function for improved hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Drewry, D.; Ruddell, B. L.

    2014-12-01

    Land surface hydrology in vegetated landscapes is strongly controlled by ecophysiological function. The coupling between photosynthesis, stomatal dynamics and leaf energy balance fundamentally links the hydrologic and carbon cycles, and provides a basis for examining the utility of observations of functional plant traits for hydrologic prediction. Here we explore the potential of solar induced fluorescence (SIF) and thermal infrared (TIR) remote sensing observations to improve the accuracy and reduce the uncertainty in hydrologic prediction. While SIF represents an emission of radiation associated with photosynthesis, TIR provides information on foliage temperature and is related to stomatal function and water stress. A set of remote observing system simulation experiments are conducted to quantify the value of remotely sensed observations of SIF and TIR when assimilated into a detailed vegetation biophysical model. The MLCan model discretizes a dense plant canopy to resolve vertical variation in photosynthesis, water vapor and energy exchange. Here we present extensions to MLCan that allow for direct computation of the canopy emission of both SIF and TIR. The detailed representation of the physical environment and biological functioning of structurally complex canopies makes MLCan an ideal simulation tool for exploring the impact of these two unique, and potentially synergistic observables. This work specifically addresses remote sensing capabilities on both recently launched (OCO-2) and near-term (ECOSTRESS) satellite platforms. We contrast the information gained through the assimilation of SIF and TIR observations to that of the assimilation of data related to physical states such as soil moisture and leaf area index.

  20. Predicting Soil Biological and Physical Properties Using Hydrological Properties

    NASA Astrophysics Data System (ADS)

    Geiger, L.; Hofmockel, K.; Kaleita, A.; Hargreaves, S.

    2012-12-01

    Soil biological and chemical properties vary at different spatial scales, which make predicting processes associated with these properties difficult. However, soil biological and chemical properties are important to fertility and ecosystem functioning. In this study, we used a Self Organizing Map (SOM) to determine whether soil hydrological characteristics can be used to characterize the distribution of a suite of soil biological and chemical properties. From a row crop field in south-central Iowa, we generated 36 sampling locations via a SOM, which were grouped into three categories according to hydrological properties by the SOM. Soil samples were then analyzed for microbial biomass, carbon and nitrogen mineralization potential, and organic and inorganic pools of carbon and nitrogen. We found that sampling locations in category 1 (potholes and toe slopes) had greater microbial biomass, total carbon, total nitrogen, and extractable organic carbon than compared locations in the two well-drained categories. Nitrogen and carbon mineralization and inorganic nitrogen pools did not differ significantly among the categories. These results demonstrate that hydrological characteristics can be used to predict relatively stable biological and chemical soil properties. However, prediction of nitrogen and carbon fluxes remains a challenge.

  1. Assessing predictability of a hydrological stochastic-dynamical system

    NASA Astrophysics Data System (ADS)

    Gelfan, Alexander

    2014-05-01

    The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar

  2. Why hydrological predictions should be evaluated using information theory

    NASA Astrophysics Data System (ADS)

    Weijs, S. V.; Schoups, G.; van de Giesen, N.

    2010-12-01

    Probabilistic predictions are becoming increasingly popular in hydrology. Equally important are methods to test such predictions, given the topical debate on uncertainty analysis in hydrology. Also in the special case of hydrological forecasting, there is still discussion about which scores to use for their evaluation. In this paper, we propose to use information theory as the central framework to evaluate predictions. From this perspective, we hope to shed some light on what verification scores measure and should measure. We start from the ''divergence score'', a relative entropy measure that was recently found to be an appropriate measure for forecast quality. An interpretation of a decomposition of this measure provides insight in additive relations between climatological uncertainty, correct information, wrong information and remaining uncertainty. When the score is applied to deterministic forecasts, it follows that these increase uncertainty to infinity. In practice, however, deterministic forecasts tend to be judged far more mildly and are widely used. We resolve this paradoxical result by proposing that deterministic forecasts either are implicitly probabilistic or are implicitly evaluated with an underlying decision problem or utility in mind. We further propose that calibration of models representing a hydrological system should be the based on information-theoretical scores, because this allows extracting all information from the observations and avoids learning from information that is not there. Calibration based on maximizing utility for society trains an implicit decision model rather than the forecasting system itself. This inevitably results in a loss or distortion of information in the data and more risk of overfitting, possibly leading to less valuable and informative forecasts. We also show this in an example. The final conclusion is that models should preferably be explicitly probabilistic and calibrated to maximize the information they provide.

  3. Multifractal prediction of hydrological extremes and the RIO research program

    NASA Astrophysics Data System (ADS)

    Tchiguirinskaia, I.; Schertzer, D. J.; Hubert, P.; Bendjoudi, H.; Lovejoy, S.

    2004-05-01

    One of the main research themes of the current RIO (Risque Inondation / Flood Risk) program of the Ministry of Environment in France is the prediction of extreme hydrological events and the development of new tools for their prediction, prevention and alert. Deterministic models based on various physical and/or statistical approaches are still not capable to capture the phenomena of extreme precipitation and floods. It is well known that one of the main difficulties for the description of hydro-meteorological extremes is the colossal variability of their intensities over a wide range of space-time scales. To contribute to the RIO program, our group uses the multifractal framework not only to explain the power-law fall-off of probability distributions for hydrological-meteorological extremes, but also to explore a link between the observed variability and the underlying physics. We analyze space-time distributions of precipitation and discharges over widely different hydrological regions. A multifractal data analysis performed in the space-time domain produces - amongst other results - a physically-based tool for the clear distinction and multifractal description of flash-floods. We illustrate these methods on two recent flooding events in France: the Abbeville phreatic floods in 2001 and the flash floods in Gard in 2002.

  4. Predicting Hydrologic Response Through a Pooled Watershed Knowledge Base: A Hierarchical Bayesian Approach

    NASA Astrophysics Data System (ADS)

    Smith, T. J.; Marshall, L. A.; Sharma, A.

    2011-12-01

    Hydrologic modelers are confronted with the challenge of producing estimates of the uncertainty associated with model predictions across a wide array of watersheds, often under very limited data conditions. Statistical methods for hydrologic modeling have evolved rapidly over the recent past in response to these challenges, from improved strategies to both estimate optimal parameter values and predictive uncertainty to approaches that aim to link model parameters to watershed characteristics and allow parameters to be transferred to data-poor watersheds. However, despite the advances that have been made in the application of such statistical tools there remains significant work to be done, particularly regarding the quantification/transfer of predictive uncertainty at/to data-limited locations. Here, we present a hierarchical Bayesian modeling technique referred to as Bayes Empirical Bayes (BEB) as a means of addressing the difficulties in making reliable hydrologic predictions under uncertainty in data-limited watersheds. The BEB technique utilizes formal hierarchical Bayesian analysis (specifically the resultant posterior probability distributions for each estimated model parameter) to pool information from auxiliary watersheds to generate informed probability distributions for each parameter at a data-limited watershed of interest. The application of such a method has thus far been untested in hydrologic applications but has been used more extensively in ecological studies. This technique represents a significant departure from earlier attempts to make predictions in data-limited watersheds in both its usage of available data and its ability to simultaneously quantify predictive uncertainty directly. By utilizing the Bayesian toolkit under a hierarchical approach, information available from auxiliary watersheds can be integrated and summarized into the prediction at the site of interest.

  5. ARCHES: Advancing Research & Capacity in Hydrologic Education and Science

    NASA Astrophysics Data System (ADS)

    Milewski, A.; Fryar, A. E.; Durham, M. C.; Schroeder, P.; Agouridis, C.; Hanley, C.; Rotz, R. R.

    2013-12-01

    Educating young scientists and building capacity on a global scale is pivotal towards better understanding and managing our water resources. Based on this premise the ARCHES (Advancing Research & Capacity in Hydrologic Education and Science) program has been established. This abstract provides an overview of the program, links to access information, and describes the activities and outcomes of student participants from the Middle East and North Africa. The ARCHES program (http://arches.wrrs.uga.edu) is an integrated hydrologic education approach using online courses, field programs, and various hands-on workshops. The program aims to enable young scientists to effectively perform the high level research that will ultimately improve quality of life, enhance science-based decision making, and facilitate collaboration. Three broad, interlinked sets of activities are incorporated into the ARCHES program: (A1) the development of technical expertise, (A2) the development of professional contacts and skills, and (A3) outreach and long-term sustainability. The development of technical expertise (A1) is implemented through three progressive instructional sections. Section 1: Students were guided through a series of online lectures and exercises (Moodle: http://wrrs.uga.edu/moodle) covering three main topics (Remote Sensing, GIS, and Hydrologic Modeling). Section 2: Students participated in a hands-on workshop hosted at the University of Georgia's Water Resources and Remote Sensing Laboratory (WRRSL). Using ENVI, ArcGIS, and ArcSWAT, students completed a series of lectures and real-world applications (e.g., Development of Hydrologic Models). Section 3: Students participated in field studies (e.g., measurements of infiltration, recharge, streamflow, and water-quality parameters) conducted by U.S. partners and international collaborators in the participating countries. The development of professional contacts and skills (A2) was achieved through the promotion of networking

  6. Uncertainty of hydrological signatures predicted for ungauged basins

    NASA Astrophysics Data System (ADS)

    Westerberg, Ida; Coxon, Gemma; Wagener, Thorsten; McMillan, Hilary; Montanari, Alberto; Castellarin, Attilio; Freer, Jim

    2015-04-01

    Reliable information about the hydrological behaviour of an ungauged catchment is needed for a wide range of water resource management decisions, and it has been a central topic of research in hydrology for the last decade through the Predictions in Ungauged Basins initiative. Such information derived as an index value from observed data in a gauged basin is known as a hydrological signature, and has been used in a variety of studies for, e.g., change detection, model calibration and diagnostic model-structural evaluation. When signature values are predicted for ungauged catchments, they are not only affected by uncertainties in the regionalisation procedure, but also by uncertainties in the observed data for the gauged catchments used for the prediction. In this study we investigated a method for regionalisation of hydrological signatures to ungauged catchments that accounted for both of these uncertainty sources. This also enabled us to assess the role of the different uncertainty sources in defining the overall regionalisation uncertainty - e.g. for what signatures and conditions are the data uncertainties more important than the regionalisation uncertainties and vice versa? The study was made using an extensive dataset of catchments in England and Wales, incorporating gauging (stage-discharge) data from all the discharge stations. The uncertainties were assessed within a Monte Carlo framework that incorporated different types of uncertainties in the data as well as uncertainties in the regionalisation procedure. The regionalisation results had a high reliability when the gauged discharge uncertainties were accounted for. The magnitude of the gauged uncertainty was often larger than the differences between deterministic gauged and regionalised values, which shows that deterministic comparisons are insufficient for evaluation of regionalisation results. The results were better for medium and high-flow signatures than for low-flow signatures. The data

  7. Advances in river ice hydrology 1999-2003

    NASA Astrophysics Data System (ADS)

    Morse, Brian; Hicks, Faye

    2005-01-01

    In the period 1999 to 2003, river ice has continued to have important socio-economic impacts in Canada and other Nordic countries. Concurrently, there have been many important advances in all areas of Canadian research into river ice engineering and hydrology. For example: (1) River ice processes were highlighted in two special journal issues (Canadian Journal of Civil Engineering in 2003 and Hydrological Processes in 2002) and at five conferences (Canadian Committee on River Ice Processes and the Environment in 1999, 2001 and 2003, and International Association of Hydraulic Research in 2000 and 2002). (2) A number of workers have clearly advanced our understanding of river ice processes by bringing together disparate information in comprehensive review articles. (3) There have been significant advances in river ice modelling. For example, both one-dimensional (e.g. RIVICE, RIVJAM, ICEJAM, HEC-RAS, etc.) and two-dimensional (2-D; www.river2d.ca) public-domain ice-jam models are now available. Work is ongoing to improve RIVER2D, and a commercial 2-D ice-process model is being developed. (4) The 1999-2003 period is notable for the number of distinctly hydrological and ecological studies. On the quantitative side, many are making efforts to determine streamflow during the winter period. On the ecological side, some new publications have addressed the link to water quality (temperature, dissolved oxygen, nutrients and pollutants), and others have dealt with sediment transport and geomorphology (particularly as it relates to break-up), stream ecology (plants, food cycle, etc.) and fish habitat.There is the growing recognition, that these types of study require collaborative efforts. In our view, the main areas requiring further work are: (1) to interface geomorphological and habitat models with quantitative river ice hydrodynamic models; (2) to develop a manager's toolbox (database management, remote sensing, forecasting, intervention methodologies, etc.) to enable

  8. Advancing reservoir operation description in physically based hydrological models

    NASA Astrophysics Data System (ADS)

    Anghileri, Daniela; Giudici, Federico; Castelletti, Andrea; Burlando, Paolo

    2016-04-01

    Last decades have seen significant advances in our capacity of characterizing and reproducing hydrological processes within physically based models. Yet, when the human component is considered (e.g. reservoirs, water distribution systems), the associated decisions are generally modeled with very simplistic rules, which might underperform in reproducing the actual operators' behaviour on a daily or sub-daily basis. For example, reservoir operations are usually described by a target-level rule curve, which represents the level that the reservoir should track during normal operating conditions. The associated release decision is determined by the current state of the reservoir relative to the rule curve. This modeling approach can reasonably reproduce the seasonal water volume shift due to reservoir operation. Still, it cannot capture more complex decision making processes in response, e.g., to the fluctuations of energy prices and demands, the temporal unavailability of power plants or varying amount of snow accumulated in the basin. In this work, we link a physically explicit hydrological model with detailed hydropower behavioural models describing the decision making process by the dam operator. In particular, we consider two categories of behavioural models: explicit or rule-based behavioural models, where reservoir operating rules are empirically inferred from observational data, and implicit or optimization based behavioural models, where, following a normative economic approach, the decision maker is represented as a rational agent maximising a utility function. We compare these two alternate modelling approaches on the real-world water system of Lake Como catchment in the Italian Alps. The water system is characterized by the presence of 18 artificial hydropower reservoirs generating almost 13% of the Italian hydropower production. Results show to which extent the hydrological regime in the catchment is affected by different behavioural models and reservoir

  9. The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction

    NASA Astrophysics Data System (ADS)

    Kunnath-Poovakka, A.; Ryu, D.; Renzullo, L. J.; George, B.

    2016-04-01

    Calibration of spatially distributed hydrologic models is frequently limited by the availability of ground observations. Remotely sensed (RS) hydrologic information provides an alternative source of observations to inform models and extend modelling capability beyond the limits of ground observations. This study examines the capability of RS evapotranspiration (ET) and soil moisture (SM) in calibrating a hydrologic model and its efficacy to improve streamflow predictions. SM retrievals from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and daily ET estimates from the CSIRO MODIS ReScaled potential ET (CMRSET) are used to calibrate a simplified Australian Water Resource Assessment - Landscape model (AWRA-L) for a selection of parameters. The Shuffled Complex Evolution Uncertainty Algorithm (SCE-UA) is employed for parameter estimation at eleven catchments in eastern Australia. A subset of parameters for calibration is selected based on the variance-based Sobol' sensitivity analysis. The efficacy of 15 objective functions for calibration is assessed based on streamflow predictions relative to control cases, and relative merits of each are discussed. Synthetic experiments were conducted to examine the effect of bias in RS ET observations on calibration. The objective function containing the root mean square deviation (RMSD) of ET result in best streamflow predictions and the efficacy is superior for catchments with medium to high average runoff. Synthetic experiments revealed that accurate ET product can improve the streamflow predictions in catchments with low average runoff.

  10. On wireless sensing networks in hydrology: from observation to prediction

    NASA Astrophysics Data System (ADS)

    Vereecken, H.; Bogena, H. R.; Huisman, J. A.; Wei, Q.; Fang, Z.; Vanderborght, J.; Kollet, S. J.

    2015-12-01

    The use of wireless sensor networks (WSN) has gained increasing attention in the field of hydrology, because WSNs offer a unique potential to monitor the spatial and temporal dynamics of soil moisture at scales beyond the field scale. In addition, they provide unique opportunities for the validation of numerical models, hydrogeophysical measurement techniques, as well as for the calibration and validation of remotely sensed soil moisture data. In this presentation, we will discuss results of temporal and spatially resolved measurements of soil moisture using WSNs installed in two different small-scale catchments under forest (Wüstebach, Germany) and grassland (Rollesbroich, Germany). In combination with measurements of hydrological fluxes, we were able to close the water balance of the Wüstebach catchment up to 3% of the yearly rainfall. In addition, changes between wet and dry states of the catchment could be observed and related to a critical soil moisture content. Using stochastic analysis of water flow in the unsaturated zone and pedotransfer functions, we were able to predict subgrid variability of soil moisture. This framework also allowed deriving the spatial variability of soil hydraulic parameters using the relationship between the variance of soil moisture and its mean soil water content. Finally, soil moisture data from the WSN in the Wüstebach catchment were used to validate a detailed hydrologic model of the catchment using empirical orthogonal functions and coherence wavelet analysis. Further development of wireless sensing technologies will include the monitoring of soil moisture potential and biogeochemical properties such as redox potential.

  11. Advancing Drought Understanding, Monitoring and Prediction

    NASA Technical Reports Server (NTRS)

    Mariotti, Annarita; Schubert, Siegfried D.; Mo, Kingtse; Peters-Lidard, Christa; Wood, Andy; Pulwarty, Roger; Huang, Jin; Barrie, Dan

    2013-01-01

    Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem

  12. Predicting Historical Droughts in the US With a Multi-model Seasonal Hydrologic Prediction System

    NASA Astrophysics Data System (ADS)

    Luo, L.; Wood, E.; Sheffield, J.; Li, H.

    2008-12-01

    Droughts are as much a part of weather and climate extremes as floods, hurricanes and tornadoes are, but they are the most costly extremes among all natural disasters in the U.S. The estimated annual direct losses to the U.S economy due to droughts are about 6-8 billion, with the drought of 1988 estimated to have damages over $39 billion. Having a seasonal drought prediction system that can accurately predict the onset, development and recovery of drought episodes will significantly help to reduce the loss due to drought. In this study, a seasonal hydrologic ensemble prediction system developed for the eastern United States is used to predict historical droughts in the US retrospectively. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. The system is applied to different regions in the US to predict historical drought episodes. These forecasts use seasonal climate forecast from a combination of the NCEP CFS and seven climate models in the European Union's Development of a European Multimodel Ensemble System for Seasonal to-Interannual Prediction (CFS+DEMETER). This study validates the approach of using seasonal climate predictions from

  13. A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Cheng, Chun-Tian; Xie, Jing-Xin; Chau, Kwok-Wing; Layeghifard, Mehdi

    2008-10-01

    SummaryA dependable long-term hydrologic prediction is essential to planning, designing and management activities of water resources. A three-stage indirect multi-step-ahead prediction model, which combines dynamic spline interpolation into multilayer adaptive time-delay neural network (ATNN), is proposed in this study for the long term hydrologic prediction. In the first two stages, a group of spline interpolation and dynamic extraction units are utilized to amplify the effect of observations in order to decrease the errors accumulation and propagation caused by the previous prediction. In the last step, variable time delays and weights are dynamically regulated by ATNN and the output of ATNN can be obtained as a multi-step-ahead prediction. We use two examples to illustrate the effectiveness of the proposed model. One example is the sunspots time series that is a well-known nonlinear and non-Gaussian benchmark time series and is often used to evaluate the effectiveness of nonlinear models. Another example is a case study of a long-term hydrologic prediction which uses the monthly discharges data from the Manwan Hydropower Plant in Yunnan Province of China. Application results show that the proposed method is feasible and effective.

  14. How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

    NASA Astrophysics Data System (ADS)

    Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

    2015-03-01

    The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all

  15. Assessing hydrologic prediction uncertainty resulting from soft land cover classification

    NASA Astrophysics Data System (ADS)

    Loosvelt, Lien; De Baets, Bernard; Pauwels, Valentijn R. N.; Verhoest, Niko E. C.

    2014-09-01

    For predictions in ungauged basins (PUB), environmental data is generally not available and needs to be inferred by indirect means. Existing technologies such as remote sensing are valuable tools for estimating the lacking data, as these technologies become more widely available and have a high areal coverage. However, indirect estimates of the environmental characteristics are prone to uncertainty. Hence, an improved understanding of the quality of the estimates and the development of methods for dealing with their associated uncertainty are essential to evolve towards accurate PUB. In this study, the impact of the uncertainty associated with the classification of land cover based on multi-temporal SPOT imagery, resulting from the use of the Random Forests classifier, on the predictions of the hydrologic model TOPLATS is investigated through a Monte Carlo simulation. The results show that the predictions of evapotranspiration, runoff and baseflow are hardly affected by the classification uncertainty when area-averaged predictions are intended, implying that uncertainty propagation is only advisable in case a spatial distribution of the predictions is relevant for decision making or is coupled to other spatially distributed models. Based on the resulting uncertainty map, guidelines for additional data collection are formulated in order to reduce the uncertainty for future model applications. Because a Monte Carlo-based uncertainty analysis is computationally very demanding, especially when complex models are involved, we developed a fast indicative uncertainty assessment method that allows for generating proxies of the Monte Carlo-based result in terms of the mean prediction and its associated uncertainty based on a single model evaluation. These proxies are shown to perform well and provide a good indication of the impact of classification uncertainty on the prediction result.

  16. Effects of subsurface heterogeneity on large-scale hydrological predictions

    NASA Astrophysics Data System (ADS)

    Hartmann, Andreas; Gleeson, Tom; Wagener, Thorsten; Wada, Yoshihide

    2015-04-01

    Heterogeneity is abundant everywhere across the hydrosphere. It exists in the soil, the vadose zone and the groundwater producing preferential flow and complex threshold behavior. In large-scale hydrological models, subsurface heterogeneity is usually not considered. Instead average or representative values are chosen for each of the simulated grid cells, not incorporating any sub-grid variability. This may lead to unreliable predictions when the models are used for assessing future water resources availability, floods or droughts, or when they are used for recommendations for more sustainable water management. In this study we use a novel, large-scale model that takes into account sub-grid heterogeneity for the simulation of groundwater recharge by using statistical distribution functions. We choose all regions over Europe that are comprised by carbonate rock (~35% of the total area) because the well understood dissolvability of carbonate rocks (karstification) allows for assessing the strength of subsurface heterogeneity. Applying the model with historic data and future climate projections we show that subsurface heterogeneity results (1) in larger present-day groundwater recharge and (2) a greater vulnerability to climate in terms of long-term decrease and hydrological extremes.

  17. Statistical procedures for evaluating daily and monthly hydrologic model predictions

    USGS Publications Warehouse

    Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

    2004-01-01

    The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

  18. Global scale hydrology - Advances in land surface modeling

    SciTech Connect

    Wood, E.F. )

    1991-01-01

    Research into global scale hydrology is an expanding area that includes researchers from the meteorology, climatology, ecology and hydrology communities. This paper reviews research in this area carried out in the United States during the last IUGG quadrennial period of 1987-1990. The review covers the representation of land-surface hydrologic processes for general circulation models (GCMs), sensitivity analysis of these representations on global hydrologic fields like precipitation, regional studies of climate that have global hydrologic implications, recent field studies and experiments whose aims are the improved understanding of land surface-atmospheric interactions, and the use of remotely sensed data for the further understanding of the spatial variability of surface hydrologic processes that are important at regional and global climate scales. 76 refs.

  19. Hydrology

    ERIC Educational Resources Information Center

    Sharp, John M., Jr.

    1978-01-01

    The past year saw a re-emphasis on the practical aspects of hydrology due to regional drought patterns, urban flooding, and agricultural and energy demands on water resources. Highlights of hydrologic symposia, publications, and events are included. (MA)

  20. An Integrated Multiscale Approach to River Flood Prediction Using a Land-Surface Hydrology Model

    NASA Astrophysics Data System (ADS)

    Mackey, B. P.; Barros, A. P.; Krishnamurti, T. N.

    2005-05-01

    This work outlines and demonstrates a comprehensive hydrometeorological flood forecasting system that is interdisciplinary in nature, multiscale in its approach, and state-of-the-art in its use of forecasting techniques. This integrated approach links both meteorological and hydrological tools in order to realize a more accurate prediction of major flood events three to six days in advance. One focus is on a new non-linear method to improve global multi-model superensemble precipitation forecasts with an emphasis on successful prediction of intense rain areas. On average, the skill from such a technique is higher and the bias lower than any of the individual member models, and the overall character of the precipitation distribution is maintained through the 5-day forecast period. In addition, global and nested regional spectral models are integrated in hindcast mode. Output from such models as well as from the superensemble is used as forcing input to a physically-based, spatially-distributed hydrology model in order to predict streamflow response during selected flood events. In this terrestrial hydrology prediction system, a dynamical water routing scheme and other physical parameterizations are used in conjunction with a 3-D hydrologic model that keeps track of surface water and energy budgets, including surface-subsurface interactions, groundwater and hydraulic river routing. Experiments are run for the Limpopo River basin in southeastern Africa, where massive flooding occurred in both years 2000 and 2001. An important intermediate step in this process is the downscaling of the precipitation forecasts from the course resolution atmospheric models to the much finer resolution (1 to 10 km) required by the hydrology model. For this purpose, we examined the space-time scaling behavior of simulated precipitation fields from the NWP models at different resolutions and devised a simple physically-based multifractal downscaling algorithm that relies on the scaling

  1. Toward an Online Community of Educators: The Modular Curriculum for Hydrologic Advancement (MOCHA)

    NASA Astrophysics Data System (ADS)

    Kelleher, C.; Wagener, T.; Gooseff, M. N.; Gregg, S.; McGlynn, B. L.; Sharma, P.; Meixner, T.; Marshall, L. A.; McGuire, K. J.; Weiler, M.

    2009-12-01

    The field of hydrology encompasses a wide range of departments and disciplines, ranging from civil engineering to geography to geosciences. As a consequence, in-class hydrology education is often strongly biased towards the background of a single instructor, limiting the educational experience of the students and not allowing for a holistic approach to hydrology education. Recently established, the Modular Curriculum for Hydrologic Advancement (MOCHA) creates an online community of hydrologists from a range of backgrounds and disciplines to define the boundaries of an unbiased hydrology education and to jointly develop resources to overcome previous instructional limitations (http://www.mocha.psu.edu/). Our first objective is to create an evolving core curriculum for hydrology education freely available to, developed, evolved and reviewed by the worldwide hydrologic community. On a larger scale, we hope to raise the standard of hydrology education and to foster international collaboration and exchange. Our work began with an initial survey including over 100 hydrology educators to assess the state of current hydrology education. Based on the survey results, the MOCHA project was designed and implemented, and initial teaching material and pedagogical guidelines for good practice in teaching were prepared. This past fall and spring, we piloted the website and teaching material across several universities. The web-based MOCHA project has recently been opened to solicit contributions from the global hydrology community. Our presentation will focus on the overall vision behind MOCHA, lessons learned from our initial piloting, and current steps to achieve our vision.

  2. Multi-scale Characterization and Prediction of Coupled Subsurface Biogeochemical-Hydrological Processes

    SciTech Connect

    Hubbard, Susan; Williams, Ken; Steefel, Carl; Banfield, Jill; Long, Phil; Slater, Lee; Pride, Steve; Jinsong Chen

    2006-06-01

    To advance solutions needed for remediation of DOE contaminated sites, approaches are needed that can elucidate and predict reactions associated with coupled biological, geochemical, and hydrological processes over a variety of spatial scales and in heterogeneous environments. Our previous laboratory experimental experiments, which were conducted under controlled and homogeneous conditions, suggest that geophysical methods have the potential for elucidating system transformations that often occur during remediation. Examples include tracking the onset and aggregation of precipitates associated with sulfate reduction using seismic and complex resistivity methods (Williams et al., 2005; Ntarlagiannis et al., 2005) as well as estimating the volume of evolved gas associated with denitrification using radar velocity. These exciting studies illustrated that geophysical responses correlated with biogeochemical changes, but also that multiple factors could impact the geophysical signature and thus a better understanding as well as integration tools were needed to advance the techniques to the point where they can be used to provide quantitative estimates of system transformations.

  3. Passive microwave (SSM/I) satellite predictions of valley glacier hydrology, Matanuska Glacier, Alaska

    USGS Publications Warehouse

    Kopczynski, S.E.; Ramage, J.; Lawson, D.; Goetz, S.; Evenson, E.; Denner, J.; Larson, G.

    2008-01-01

    We advance an approach to use satellite passive microwave observations to track valley glacier snowmelt and predict timing of spring snowmelt-induced floods at the terminus. Using 37 V GHz brightness temperatures (Tb) from the Special Sensor Microwave hnager (SSM/I), we monitor snowmelt onset when both Tb and the difference between the ascending and descending overpasses exceed fixed thresholds established for Matanuska Glacier. Melt is confirmed by ground-measured air temperature and snow-wetness, while glacier hydrologic responses are monitored by a stream gauge, suspended-sediment sensors and terminus ice velocity measurements. Accumulation area snowmelt timing is correlated (R2 = 0.61) to timing of the annual snowmelt flood peak and can be predicted within ??5 days. Copyright 2008 by the American Geophysical Union.

  4. Advances in predicting acute GVHD

    PubMed Central

    Harris, Andrew C.; Ferrara, James L.M.; Levine, John E.

    2012-01-01

    Summary Acute graft-versus-host disease (GVHD) is a leading cause of non-relapse mortality following allogeneic haematopoietic cell transplantation. Attempts to improve treatment response in clinically-established GVHD have not improved overall survival, often due to the increased risk of infectious complications. Alternative approaches to decrease GVHD-related morbidity and mortality have focused on the ability to predict GVHD prior to clinical manifestation in an effort to provide an opportunity to abort GVHD development, and to gain new insights into GVHD pathophysiology. This review outlines the research efforts to date that have identified clinical and laboratory-based factors that are predictive of acute GVHD and describes future directions in developing algorithms that will improve the ability to predict the development of clinically relevant GVHD. PMID:23205489

  5. Improved understanding and prediction of the hydrologic response of highly urbanized catchments through development of the Illinois Urban Hydrologic Model

    NASA Astrophysics Data System (ADS)

    Cantone, Joshua; Schmidt, Arthur

    2011-08-01

    What happens to the rain in highly urbanized catchments? That is the question that urban hydrologists must ask themselves when trying to integrate the hydrologic and hydraulic processes that affect the hydrologic response of urban catchments. The Illinois Urban Hydrologic Model (IUHM) has been developed to help answer this question and improve understanding and prediction of hydrologic response in highly urbanized catchments. Urban catchments are significantly different than natural watersheds, but there are similarities that allow features of the pioneering geomorphologic instantaneous unit hydrograph concept developed for natural watersheds to be adapted to the urban setting. This probabilistically based approach is a marked departure from the traditional deterministic models used to design and simulate urban sewer systems and does not have the burdensome input data requirements that detailed deterministic models possess. Application of IUHM to the CDS-51 catchment located in the village of Dolton, Illinois, highlights the model's ability to predict the hydrologic response of the catchment as well as the widely accepted SWMM model and is in accordance with observed data recorded by the United States Geological Survey. In addition, the unique structure and organization of urban sewer networks make it possible to characterize a set of ratios for urban catchments that allow IUHM to be applied when detailed input data are not available.

  6. Advancing Collaboration through Hydrologic Data and Model Sharing

    NASA Astrophysics Data System (ADS)

    Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Castronova, A. M.; Miles, B.; Li, Z.; Morsy, M. M.

    2015-12-01

    HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around "resources" which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative's Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called "BagIt". HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare's content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

  7. A new data assimilation approach for improving hydrologic prediction using remotely-sensed soil moisture retrievals

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A number of recent studies have focused on enhancing hydrologic prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem purely from a state or parameter estimation perspective in which remo...

  8. Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Tao, P.; Tie-Yuan, S.; Zhi-Yuan, Y.; Jun-Chao, W.

    2015-05-01

    The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.

  9. Hydrology

    ERIC Educational Resources Information Center

    Sharp, John M.

    1977-01-01

    Lists many recent research projects in hydrology, including flow in fractured media, improvements in remote-sensing techniques, effects of urbanization on water resources, and developments in drainage basins. (MLH)

  10. Timing and Prediction of Climate Change and Hydrological Impacts: Periodicity in Natural Variations

    EPA Science Inventory

    Hydrological impacts from climate change are of principal interest to water resource policy-makers and practicing engineers, and predictive climatic models have been extensively investigated to quantify the impacts. In palaeoclmatic investigations, climate proxy evidence has une...

  11. Demasking the integrated value of discharge - Advanced sensitivity analysis on the components of hydrological models

    NASA Astrophysics Data System (ADS)

    Guse, Björn; Pfannerstill, Matthias; Gafurov, Abror; Fohrer, Nicola; Gupta, Hoshin

    2016-04-01

    The hydrologic response variable most often used in sensitivity analysis is discharge which provides an integrated value of all catchment processes. The typical sensitivity analysis evaluates how changes in the model parameters affect the model output. However, due to discharge being the aggregated effect of all hydrological processes, the sensitivity signal of a certain model parameter can be strongly masked. A more advanced form of sensitivity analysis would be achieved if we could investigate how the sensitivity of a certain modelled process variable relates to the changes in a parameter. Based on this, the controlling parameters for different hydrological components could be detected. Towards this end, we apply the approach of temporal dynamics of parameter sensitivity (TEDPAS) to calculate the daily sensitivities for different model outputs with the FAST method. The temporal variations in parameter dominance are then analysed for both the modelled hydrological components themselves, and also for the rates of change (derivatives) in the modelled hydrological components. The daily parameter sensitivities are then compared with the modelled hydrological components using regime curves. Application of this approach shows that when the corresponding modelled process is investigated instead of discharge, we obtain both an increased indication of parameter sensitivity, and also a clear pattern showing how the seasonal patterns of parameter dominance change over time for each hydrological process. By relating these results with the model structure, we can see that the sensitivity of model parameters is influenced by the function of the parameter. While capacity parameters show more sensitivity to the modelled hydrological component, flux parameters tend to have a higher sensitivity to rates of change in the modelled hydrological component. By better disentangling the information hidden in the discharge values, we can use sensitivity analyses to obtain a clearer signal

  12. Catchment coevolution: A useful framework for improving predictions of hydrological change?

    NASA Astrophysics Data System (ADS)

    Troch, Peter A.; Lahmers, Tim; Meira, Antonio; Mukherjee, Rajarshi; Pedersen, Jonas W.; Roy, Tirthankar; Valdés-Pineda, Rodrigo

    2015-07-01

    The notion that landscape features have coevolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this paper we review recent literature on this subject and attempt to synthesize what we have learned into a general framework that would improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that

  13. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  14. Snow multivariable data assimilation for hydrological predictions in mountain areas

    NASA Astrophysics Data System (ADS)

    Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

    2016-04-01

    The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

  15. Influence of event characteristics on predictive uncertainty of a hydrological model

    NASA Astrophysics Data System (ADS)

    Samanta, S.; Mackay, D. S.

    2001-05-01

    An automated calibration and uncertainty estimation framework based on Monte Carlo sampling was developed and tested for its ability to estimate the predictive uncertainty of a semi-distributed hydro-ecological simulation model with respect to its hydrological predictions. This procedure was utilized for an adaptive predictive uncertainty estimation by classifying observed stream flow data into different event type classes that are similar in terms of dominant hydrological processes. Objective functions applied to the classified data were then used to calibrate the model, thereby adapting the calibration to different hydrological processes. The predictive uncertainty associated with each class of event was estimated by considering the acceptable model set as a fuzzy set of model realizations with the membership grade function equivalent to the corresponding calibration objective. Predictive uncertainty estimated using this technique showed considerable variation, but periods considered hydrologically similar, showed similar levels of predictive uncertainty. This approach also resulted in more refined and consistent identification of acceptable parameter sets compared to a traditional automated calibration. The results indicate that such a calibration technique has the potential to provide an estimate of relative predictive abilities of various submodels embedded within complex simulation models and provide a basis for testing model components and their interactions. However, a more robust event classification combined with suitable objective function definitions would be necessary to develop an event type based calibration technique that can be used for the purpose of characterizing the predictive uncertainty of a hydrological model.

  16. Advances in Modeling of Coupled Hydrologic-Socioeconomic Systems

    NASA Astrophysics Data System (ADS)

    Amadio, Mattia; Mysiak, Jaroslav; Pecora, Silvano; Agnetti, Alberto

    2013-04-01

    River flooding is the most common natural disaster in Europe, causing deaths and huge amount of economic losses. Disastrous flood events are often related to extreme meteorological conditions; therefore, climate change is expected to have an important influence over the intensity and frequency of major floods. While approximated large-scale assessments of flood risk scenarios have been carried out, the knowledge of the effects at smaller scales is poor or incomplete, with few localized studies. Also, the methods are still coarse and uneven. The approach of this study starts from the definition of the risk paradigm and the elaboration of local climatic scenarios to track a methodology aimed at elaborating and combining the three elements concurring to the determination of risk: hydrological hazard, value exposure and vulnerability. First, hydrological hazard scenarios are provided by hydrological and hydrodynamic models, used in to a flood forecasting system capable to define "what-if" scenario in a flexible way. These results are then integrated with land-use data (exposure) and depth-damage functions (vulnerability) in a GIS environment, to assess the final risk value (potential flood damage) and visualize it in form of risk maps. In this paper results from a pilot study in the Polesine area are presented, where four simulated levee breach scenarios are compared. The outcomes of the analysis may be instrumental to authorities to increase the knowledge of possible direct losses and guide decision making and planning processes also. As future perspective, the employed methodology can also be extended at the basin scale through integration with the existent flood warning system to gain a real-time estimate of floods direct costs.

  17. Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) Science Plan: A Community-based Infrastructure Initiative

    NASA Astrophysics Data System (ADS)

    Wilson, J. L.; Dressler, K.; Hooper, R. P.

    2005-12-01

    The river basin is a fundamental unit of the landscape and water in that defined landscape plays a central role in shaping the land surface, in dissolving minerals, in transporting chemicals, and in determining species distribution. Therefore, the river basin is a natural observatory for examining hydrologic phenomena and the complex interaction of physical, chemical, and biological processes that control them. CUAHSI, incorporated in 2001, is a community-based research infrastructure initiative formed to mobilize the hydrologic community through addressing key science questions and leveraging nationwide hydrologic resources from its member institutions and collaborative partners. Through an iterative community-based process, it has been previously proposed to develop a network of hydrologic infrastructure that organizes around scales on the order of 10,000 km2 to examine critical interfaces such as the land-surface, atmosphere, and human impact. Data collection will characterize the stores, fluxes, physical pathways, and residence time distributions of water, sediment, nutrients, and contaminants coherently at nested scales. These fundamental properties can be used by a wide range of scientific disciplines to address environmental questions. This more complete characterization will enable new linkages to be identified and hypotheses to be tested more incisively. With such a research platform, hydrologic science can advance beyond measuring streamflow or precipitation input to understanding how the river basin functions in both its internal processes and in responding to environmental stressors. That predictive understanding is needed to make informed decisions as development and even natural pressures stress existing water supplies and competing demands for water require non-traditional solutions that take into consideration economic, environmental, and social factors. Advanced hydrologic infrastructure will enable research for a broad range of multidisciplinary

  18. Ensemble hydrological prediction of streamflow percentile at ungauged basins in Pakistan

    NASA Astrophysics Data System (ADS)

    Waseem, Muhammad; Ajmal, Muhammad; Kim, Tae-Woong

    2015-06-01

    Streamflow records with sufficient spatial and temporal coverage at the site of interest are usually scarce in Pakistan. As an alternative, various regional methods have been frequently adopted to derive hydrological information, which in essence attempt to transfer hydrological information from gauged to ungauged catchments. In this study, a new concept of ensemble hydrological prediction (EHP) was introduced which is an improved regional method for hydrological prediction at ungauged sites. It was mainly based on the performance weights (triple-connection weights (TCW)) derived from Nash Sutcliffe efficiency (NSE) and hydrological variable (here percentiles) calculated from three traditional regional transfer methods (RTMs) with suitable modification (i.e., three-step drainage area ratio (DAR) method, inverse distance weighting (IDW) method, and three-step regional regression analysis (RRA)). The overall results indicated that the proposed EHP method was robust for estimating hydrological percentiles at ungauged sites as compared to traditional individual RTMs. The comparative study based on NSE, percent bias (PBIAS) and the relative error (RE) as performance criteria resulted that the EHP is a constructive alternative for hydrological prediction of ungauged basins.

  19. Predicting hydrology of fractured rock masses from geology

    NASA Astrophysics Data System (ADS)

    La Pointe, Paul R.

    Fracture network connectivity often dominates movement rate, flow volume, and mass transport through rock masses. These networks influence the effectiveness of petroleum reservoir development, safe disposal of nuclear waste, delineation of water supply or establishment of well-head protection plans, recovery from geothermal reservoirs, solution mining, construction of underground openings, and the remediation of contaminated rock. Well tests can provide a great deal of useful information on the hydraulic properties of fracture systems, but they are often expensive or logistically infeasible. These tests also may not provide an accurate description of the hydrologic properties of the rock volume under consideration. Methods to model fractured rock can be improved by quantifying the relation between geologic parameters and the hydrologically conductive fractures. This study illustrates the application of four statistical and pattern recognition methods—evaluation of correlation coefficients, contingency table analysis, multivariate regression, and neural net analysis. The data for the study consist of borehole and well-test information from eight boreholes used for characterizing a proposed low-level radioactive waste repository in Wake County, North Carolina. The analyses show that high localized flow rates are related to the presence of increased fracture intensity, and that this intensity is controlled by a complex interplay of structural geology and lithology. Some of the initial hypotheses concerning the relation of geology to hydrology were not substantiated by the data, leading to a refined conceptual model that differed in significant ways from the initial model. Although the techniques used are of general applicability, the precise nature of the correlation between geology and hydrology is site dependent.

  20. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

    NASA Astrophysics Data System (ADS)

    Thompson, S. E.; Sivapalan, M.; Harman, C. J.; Srinivasan, V.; Hipsey, M. R.; Reed, P.; Montanari, A.; Blöschl, G.

    2013-12-01

    Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle - a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science

  1. Predictive Dynamic Security Assessment through Advanced Computing

    SciTech Connect

    Huang, Zhenyu; Diao, Ruisheng; Jin, Shuangshuang; Chen, Yousu

    2014-11-30

    Abstract— Traditional dynamic security assessment is limited by several factors and thus falls short in providing real-time information to be predictive for power system operation. These factors include the steady-state assumption of current operating points, static transfer limits, and low computational speed. This addresses these factors and frames predictive dynamic security assessment. The primary objective of predictive dynamic security assessment is to enhance the functionality and computational process of dynamic security assessment through the use of high-speed phasor measurements and the application of advanced computing technologies for faster-than-real-time simulation. This paper presents algorithms, computing platforms, and simulation frameworks that constitute the predictive dynamic security assessment capability. Examples of phasor application and fast computation for dynamic security assessment are included to demonstrate the feasibility and speed enhancement for real-time applications.

  2. On the prediction of the Toce alpine basin floods with distributed hydrologic models

    NASA Astrophysics Data System (ADS)

    Montaldo, Nicola; Ravazzani, Giovanni; Mancini, Marco

    2007-02-01

    With the objective of improving flood predictions, in recent years sophisticated continuous hydrologic models that include complex land-surface sub-models have been developed. This has produced a significant increase in parameterization; consequently, applications of distributed models to ungauged basins lacking specific data from field campaigns may become redundant.The objective of this paper is to produce a parsimonious and robust distributed hydrologic model for flood predictions in Italian alpine basins. Application is made to the Toce basin (area 1534 km2). The Toce basin was a case study of the RAPHAEL European Union research project, during which a comprehensive set of hydrologic, meteorological and physiographic data were collected, including the hydrologic analysis of the 1996-1997 period. Two major floods occurred during this period. We compare the FEST04 event model (which computes rainfall abstraction and antecedent soil moisture conditions through the simple Soil Conservation Service curve number method) and two continuous hydrologic models, SDM and TDM (which differ in soil water balance scheme, and base flow and runoff generation computations).The simple FEST04 event model demonstrated good performance in the prediction of the 1997 flood, but shows limits in the prediction of the long and moderate 1996 flood. More robust predictions are obtained with the parsimonious SDM continuous hydrologic model, which uses a simple one-layer soil water balance model and an infiltration excess mechanism for runoff generation, and demonstrates good performance in both long-term runoff modelling and flood predictions. Instead, the use of a more sophisticated continuous hydrologic model, the TDM, that simulates soil moisture dynamics in two layers of soil, and computes runoff and base flow using some TOPMODEL concepts, does not seem to be advantageous for this alpine basin. Copyright

  3. A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

    NASA Astrophysics Data System (ADS)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

    2015-11-01

    This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

  4. Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

    NASA Astrophysics Data System (ADS)

    Thompson, S. E.; Sivapalan, M.; Harman, C. J.; Srinivasan, V.; Hipsey, M. R.; Reed, P.; Montanari, A.; Blöschl, G.

    2013-06-01

    Globally, many different kinds of water resources management issues call for policy and infrastructure based responses. Yet responsible decision making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to-century long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle - a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management.

  5. An Integrated Hydrologic Bayesian Multi-Model Combination Framework: Confronting Input, parameter and model structural uncertainty in Hydrologic Prediction

    SciTech Connect

    Ajami, N K; Duan, Q; Sorooshian, S

    2006-05-05

    This paper presents a new technique--Integrated Bayesian Uncertainty Estimator (IBUNE) to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. The uncertainties from the input (forcing) data--mainly the precipitation observations and from the model parameters are reduced through a Monte Carlo Markov Chain (MCMC) scheme named Shuffled Complex Evolution Metropolis (SCEM) algorithm which has been extended to include a precipitation error model. Afterwards, the Bayesian Model Averaging (BMA) scheme is employed to further improve the prediction skill and uncertainty estimation using multiple model output. A series of case studies using three rainfall-runoff models to predict the streamflow in the Leaf River basin, Mississippi are used to examine the necessity and usefulness of this technique. The results suggests that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and their associated uncertainty bounds which does not consistently capture and represent the real-world behavior of the watershed.

  6. Research Infrastructure for the Advancement of Hydrologic Science: Planning Highlights and Update

    NASA Astrophysics Data System (ADS)

    Bales, R. C.

    2001-12-01

    In response to the need for research infrastructure in hydrologic sciences, a group of over 35 universities has formed a Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). With support from the U.S. National Science Foundation, CUAHSI has initiated a science planning process aimed at building research infrastructure in three main areas: i) Long Term Hydrologic Observatories, to provide the consistent, integrated, long-term information from point to continental scales ii) a Hydrologic Information System program, to support the data, information, and analysis requirements of the community and iii) a Hydrologic Measurement Technology program to develop and operate state-of-the-art systems and provide support services for hydrologic research. Scientifically, this infrastructure initiative aims to support research to provide new understanding about priority questions in hydrologic and related sciences, including: i) spatial and temporal properties of precipitation and snow processes, ii) surface water generation and transport at scales from hectares to continental-scale basins, iii) linked water, carbon and other chemical cycles, and changes in response to varying temperature, precipitation and land-use patterns, iii) environmental stresses on aquatic and riparian ecosystems related to groundwater pumping and other perturbations, iv) basin-scale subsurface water and solute movement, particularly as related to patterns of precipitation, evapotranspiration and recharge, and v) feedback between regional evaporation and transpiration and patterns of precipitation and humidity. It has become apparent that the science infrastructure in hydrologic and related sciences is currently inadequate to meet many of these priority science questions and societal needs. Specifically, investments are needed to: i) maintain, supplement and upgrade existing field facilities, ii) establish measurement programs that can deliver consistent data over the long

  7. Advances in variable selection methods II: Effect of variable selection method on classification of hydrologically similar watersheds in three Mid-Atlantic ecoregions

    EPA Science Inventory

    Hydrological flow predictions in ungauged and sparsely gauged watersheds use regionalization or classification of hydrologically similar watersheds to develop empirical relationships between hydrologic, climatic, and watershed variables. The watershed classifications may be based...

  8. Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

    NASA Astrophysics Data System (ADS)

    Laiolo, P.; Gabellani, S.; Campo, L.; Silvestro, F.; Delogu, F.; Rudari, R.; Pulvirenti, L.; Boni, G.; Fascetti, F.; Pierdicca, N.; Crapolicchio, R.; Hasenauer, S.; Puca, S.

    2016-06-01

    The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012-June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash-Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

  9. Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?

    NASA Astrophysics Data System (ADS)

    Mazzoleni, M.; Verlaan, M.; Alfonso, L.; Monego, M.; Norbiato, D.; Ferri, M.; Solomatine, D. P.

    2015-11-01

    Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash-Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.

  10. Using site-specific soil samples as a substitution for improved hydrological and nonpoint source predictions.

    PubMed

    Chen, Lei; Wang, Guobo; Zhong, Yucen; Zhao, Xin; Shen, Zhenyao

    2016-08-01

    Soil databases are one of the most important inputs for watershed models, and the quality of soil properties affects how well a model performs. The objectives of this study were to (1) quantify the sensitivity of model outputs to soil properties and to (2) use site-specific soil properties as a substitution for more accurate hydrological and nonpoint source (H/NPS) predictions. Soil samples were collected from a typical mountainous watershed in China, and the impacts of soil sample parameters on H/NPS predictions were quantified using the Soil and Water Assessment Tool (SWAT). The most sensitive parameters related to predicting flow, sediment, and total phosphorus (TP) mainly were the soil hydrological, the channel erosion processes, and the initial soil chemical environment, respectively. When the site-specific soil properties were used, the uncertainties (coefficient of variation) related to predicting the hydrology, sediment and TP decreased by 75∼80 %, 75∼84 %, and 46∼61 %, respectively. Based on changes in the Nash-Sutcliff coefficient, the model performance improved by 4.9 and 19.45 % for the hydrological and sediment model, accordingly. However, site-specific soil properties did not contribute to better TP predictions because of the high spatial variability of the soil P concentrations across the large watershed. Thus, although site-specific soil samples can be used to obtain more accurate H/NPS predictions, more sampling sites are required to apply this method in large watersheds. PMID:27146539

  11. Seamless hydrological predictions for a monsoon driven catchment in North-East India

    NASA Astrophysics Data System (ADS)

    Köhn, Lisei; Bürger, Gerd; Bronstert, Axel

    2016-04-01

    Improving hydrological forecasting systems on different time scales is interesting and challenging with regards to humanitarian as well as scientific aspects. In meteorological research, short-, medium-, and long-term forecasts are now being merged to form a system of seamless weather and climate predictions. Coupling of these meteorological forecasts with a hydrological model leads to seamless predictions of streamflow, ranging from one day to a season. While there are big efforts made to analyse the uncertainties of probabilistic streamflow forecasts, knowledge of the single uncertainty contributions from meteorological and hydrological modeling is still limited. The overarching goal of this project is to gain knowledge in this subject by decomposing and quantifying the overall predictive uncertainty into its single factors for the entire seamless forecast horizon. Our study area is the Mahanadi River Basin in North-East India, which is prone to severe floods and droughts. Improved streamflow forecasts on different time scales would contribute to early flood warning as well as better water management operations in the agricultural sector. Because of strong inter-annual monsoon variations in this region, which are, unlike the mid-latitudes, partly predictable from long-term atmospheric-oceanic oscillations, the Mahanadi catchment represents an ideal study site. Regionalized precipitation forecasts are obtained by applying the method of expanded downscaling to the ensemble prediction systems of ECMWF and NCEP. The semi-distributed hydrological model HYPSO-RR, which was developed in the Eco-Hydrological Simulation Environment ECHSE, is set up for several sub-catchments of the Mahanadi River Basin. The model is calibrated automatically using the Dynamically Dimensioned Search algorithm, with a modified Nash-Sutcliff efficiency as objective function. Meteorological uncertainty is estimated from the existing ensemble simulations, while the hydrological uncertainty is

  12. Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Sankarasubramanian, A.

    2012-12-01

    Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs

  13. Predicting the natural flow regime: Models for assessing hydrological alteration in streams

    USGS Publications Warehouse

    Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

    2010-01-01

    Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also

  14. Practical guidance on representing the heteroscedasticity of residual errors of hydrological predictions

    NASA Astrophysics Data System (ADS)

    McInerney, David; Thyer, Mark; Kavetski, Dmitri; Kuczera, George

    2016-04-01

    Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic streamflow predictions. In particular, residual errors of hydrological predictions are often heteroscedastic, with large errors associated with high runoff events. Although multiple approaches exist for representing this heteroscedasticity, few if any studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating a range of approaches for representing heteroscedasticity in residual errors. These approaches include the 'direct' weighted least squares approach and 'transformational' approaches, such as logarithmic, Box-Cox (with and without fitting the transformation parameter), logsinh and the inverse transformation. The study reports (1) theoretical comparison of heteroscedasticity approaches, (2) empirical evaluation of heteroscedasticity approaches using a range of multiple catchments / hydrological models / performance metrics and (3) interpretation of empirical results using theory to provide practical guidance on the selection of heteroscedasticity approaches. Importantly, for hydrological practitioners, the results will simplify the choice of approaches to represent heteroscedasticity. This will enhance their ability to provide hydrological probabilistic predictions with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality).

  15. Predicting hydrological signatures in ungauged catchments using spatial interpolation, index model, and rainfall-runoff modelling

    NASA Astrophysics Data System (ADS)

    Zhang, Yongqiang; Vaze, Jai; Chiew, Francis H. S.; Teng, Jin; Li, Ming

    2014-09-01

    Understanding a catchment's behaviours in terms of its underlying hydrological signatures is a fundamental task in surface water hydrology. It can help in water resource management, catchment classification, and prediction of runoff time series. This study investigated three approaches for predicting six hydrological signatures in southeastern Australia. These approaches were (1) spatial interpolation with three weighting schemes, (2) index model that estimates hydrological signatures using catchment characteristics, and (3) classical rainfall-runoff modelling. The six hydrological signatures fell into two categories: (1) long-term aggregated signatures - annual runoff coefficient, mean of log-transformed daily runoff, and zero flow ratio, and (2) signatures obtained from daily flow metrics - concavity index, seasonality ratio of runoff, and standard deviation of log-transformed daily flow. A total of 228 unregulated catchments were selected, with half the catchments randomly selected as gauged (or donors) for model building and the rest considered as ungauged (or receivers) to evaluate performance of the three approaches. The results showed that for two long-term aggregated signatures - the log-transformed daily runoff and runoff coefficient, the index model and rainfall-runoff modelling performed similarly, and were better than the spatial interpolation methods. For the zero flow ratio, the index model was best and the rainfall-runoff modelling performed worst. The other three signatures, derived from daily flow metrics and considered to be salient flow characteristics, were best predicted by the spatial interpolation methods of inverse distance weighting (IDW) and kriging. Comparison of flow duration curves predicted by the three approaches showed that the IDW method was best. The results found here provide guidelines for choosing the most appropriate approach for predicting hydrological behaviours at large scales.

  16. Advancements in predictive plasma formation modeling

    NASA Astrophysics Data System (ADS)

    Purvis, Michael A.; Schafgans, Alexander; Brown, Daniel J. W.; Fomenkov, Igor; Rafac, Rob; Brown, Josh; Tao, Yezheng; Rokitski, Slava; Abraham, Mathew; Vargas, Mike; Rich, Spencer; Taylor, Ted; Brandt, David; Pirati, Alberto; Fisher, Aaron; Scott, Howard; Koniges, Alice; Eder, David; Wilks, Scott; Link, Anthony; Langer, Steven

    2016-03-01

    We present highlights from plasma simulations performed in collaboration with Lawrence Livermore National Labs. This modeling is performed to advance the rate of learning about optimal EUV generation for laser produced plasmas and to provide insights where experimental results are not currently available. The goal is to identify key physical processes necessary for an accurate and predictive model capable of simulating a wide range of conditions. This modeling will help to drive source performance scaling in support of the EUV Lithography roadmap. The model simulates pre-pulse laser interaction with the tin droplet and follows the droplet expansion into the main pulse target zone. Next, the interaction of the expanded droplet with the main laser pulse is simulated. We demonstrate the predictive nature of the code and provide comparison with experimental results.

  17. Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

    NASA Astrophysics Data System (ADS)

    Wright, David; Thyer, Mark; Westra, Seth

    2015-04-01

    Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this

  18. Extending medium-range predictability of extreme hydrological events in Europe

    PubMed Central

    Lavers, David A.; Pappenberger, Florian; Zsoter, Ervin

    2014-01-01

    Widespread flooding occurred across northwest Europe during the winter of 2013/14, resulting in large socioeconomic damages. In the historical record, extreme hydrological events have been connected with intense water vapour transport. Here we show that water vapour transport has higher medium-range predictability compared with precipitation in the winter 2013/14 forecasts from the European Centre for Medium-Range Weather Forecasts. Applying the concept of potential predictability, the transport is found to extend the forecast horizon by 3 days in some European regions. Our results suggest that the breakdown in precipitation predictability is due to uncertainty in the horizontal mass convergence location, an essential mechanism for precipitation generation. Furthermore, the predictability increases with larger spatial averages. Given the strong association between precipitation and water vapour transport, especially for extreme events, we conclude that the higher transport predictability could be used as a model diagnostic to increase preparedness for extreme hydrological events. PMID:25387309

  19. A spatially consistent seamless predictions of continental-scale hydrologic fluxes and states

    NASA Astrophysics Data System (ADS)

    Kumar, Rohini; Mai, Juliane; Rakovec, Oldrich; Zink, Matthias; Cuntz, Matthias; Thober, Stephan; Attinger, Sabine; Schroen, Martin; Schaefer, David; Samaniego, Luis

    2016-04-01

    One of the major challenges in the contemporary hydrology is to establish a continental-scale hydrologic model that can provide spatially consistent, seamless prediction of hydrologic fluxes and states to better characterise extreme events like floods and droughts. This requires, among other things, 1) a robust parameterization technique that allows the model to seamlessly operate across a range of spatial resolutions and 2) an efficient parameter estimation technique to derive a representative set of spatially consistent model parameters that avoid inconsistencies in simulated hydrologic fields (e.g., soil moisture). In this study, we demostrate the applicability of a mesoscale hydrologic model parameterized using a multiscale regionalization technique to derive daily gridded fields of hydrologic fluxes/states over the Pan-EU domain since 1950. A multi-basin parameter estimation (MBE) strategy that utilizes observed streamflows from a set of hydrologically diverse basins is introduced to infer a representative set of regional calibration parameters which is applicable over the entire domain. We tested three sampling schemes to select a set of calibration basins incremented sequentially from 2 to 20 basins, based on the 1) random selection procedure, 2) gradient along the hydro-climatic regimes, and 3) diversity in hydro-climatic and basin physiographical properties (e.g., terrain, soil, land cover properties). Results of the MBE approach are contrasted against the benchmark at-site calibration strategy across 400 EU basins varying from approximately 100 to 500,000 km2. At-site calibrated parameters performed best for site-specific streamflow predictions, but their transferability to other sites resulted in poor performance. Moreover, the at-site calibration strategy generated a patchy, spatially inconsistent distribution of parameter fields that further induced large discontinuities in simulated hydrologic fields of soil moisture among other sates/fluxes. These

  20. Illinois River Basin Hydrologic Observatory: A Center for Understanding and Predicting the Complex Hydrologic Cycle of Intensively Managed Landscapes

    NASA Astrophysics Data System (ADS)

    Kumar, P.

    2004-12-01

    This paper is submitted on behalf of several individuals representing many institutions. We envision that the Illinois River Basin Hydrologic Observatory (IRB-HO) will be a center of excellence that provides improved scientific understanding of the hydrologic cycle with predictive capability to support better management and decision-making by stakeholders, in an intensively managed landscape. The Illinois River begins at the confluence of the Des Plaines and Kankakee rivers near Chicago, Illinois, and flows 380 km. southwest to the Mississippi River at Grafton, Illinois. It drains an area of over 80,000 sq. km. The basin is characterized by high productivity agriculture and rapid growth of urban areas, and located in northern temperate climate with low relief glaciated landscape. The observatory will address important questions that will lead to socially useful probabilistic assessments of future conditions in the basin. The IRB-HO will serve the following two functions: \\begin{enumerate} Enable multi-scale interdisciplinary research by providing infrastructure that will attract scientists and water resource professionals to pursue research in the basin. Providing this "community science resource" will be an important function that attracts both remote and on-site participation by investigators from the hydrologic science community, nationally and internationally. Answer fundamental interdisciplinary questions of high societal relevance as part of the core effort. The core science questions will be organized around the broad thrust areas of (i) water, energy and sediment flux and dynamics, (ii) biogeochemistry, (iii) hydroecology, (iv) water resources management, (v) Transport of chemical and biological contaminants. The IRB-HO will be managed as a center with broad involvement of the community in conception, design and implementation. Further, the core data collected will be made publicly available immediately to realize maximum benefits from the HO. Education

  1. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions within the 341 km2 Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthet...

  2. Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification

    EPA Science Inventory

    A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

  3. Stochastic Residual-Error Analysis For Estimating Hydrologic Model Predictive Uncertainty

    EPA Science Inventory

    A hybrid time series-nonparametric sampling approach, referred to herein as semiparametric, is presented for the estimation of model predictive uncertainty. The methodology is a two-step procedure whereby a distributed hydrologic model is first calibrated, then followed by brute ...

  4. Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

    EPA Science Inventory

    We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

  5. Decadal predictability of land hydrology over North America in CESM

    NASA Astrophysics Data System (ADS)

    Chikamoto, Yoshimitsu; Timmermann, Axel; Stevenson, Samantha

    2013-04-01

    Potential longterm predictability of total water storage in North America is examined using a 900-year-long pre-industrial control simulation, conducted with the NCAR community earth system model (CESM). The dominant modes of simulated North American precipitation and soil water storage are characterized by similar meridional seesaw patterns. Whereas corresponding precipitation variability can be described to first order as a white noise stochastic process, power spectra of integrated soil moisture exhibit significant redness on timescales of years to decades. As a result statistical damped persistence hindcasts, following a 1st order Markov process, are skillful with lead times of up to several years. This skill estimate is consistent with ensemble hindcasts conducted with the CESM model for various initial conditions. A strong depth dependence is found for the predictive skill of moisture variations, in particular for the Northern US. Decadal variations in total water storage are shown to affect wildfire frequency. Our results suggest that predictions of decadal changes in integrated water storage are feasible, even with local statistical models, and may render useful for risk management related to water resources, forestry, and agriculture.

  6. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    NASA Astrophysics Data System (ADS)

    Xuan, Y.; Cluckie, I. D.; Wang, Y.

    2009-03-01

    Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  7. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    NASA Astrophysics Data System (ADS)

    Cluckie, I. D.; Xuan, Y.; Wang, Y.

    2006-10-01

    Advances in meso-scale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  8. A Hierarchical Approach Embedding Hydrologic and Population Modeling for a West Nile Virus Vector Prediction

    NASA Astrophysics Data System (ADS)

    Jian, Y.; Silvestri, S.; Marani, M.; Saltarin, A.; Chillemi, G.

    2012-12-01

    We applied a hierarchical state space model to predict the abundance of Cx.pipiens (a West Nile Virus vector) in the Po River Delta Region, Northeastern Italy. The study area has large mosquito abundance, due to a favorable environment and climate as well as dense human population. Mosquito data were collected on a weekly basis at more than 20 sites from May to September in 2010 and 2011. Cx.pipiens was the dominant species in our samples, accounting for about 90% of the more than 300,000 total captures. The hydrological component of the model accounted for evapotranspiration, infiltration and deep percolation to infer, in a 0D context, the local dynamics of soil moisture as a direct exogenous forcing of mosquito dynamics. The population model had a Gompertz structure, which included exogenous meteorological forcings and delayed internal dynamics. The models were coupled within a hierarchical statistical structure to overcome the relatively short length of the samples by exploiting the large number of concurrent observations available. The results indicated that Cx.pipiens abundance had significant density dependence at 1 week lag, which approximately matched its development time from larvae to adult. Among the exogenous controls, temperature, daylight hours, and soil moisture explained most of the dynamics. Longer daylight hours and lower soil moisture values resulted in higher abundance. The negative correlation of soil moisture and mosquito population can be explained with the abundance of water in the region (e.g. due to irrigation) and the preference for eutrophic habitats by Cx.pipien. Variations among sites were explained by land use factors as represented by distance to the nearest rice field and NDVI values: the carrying capacity decreased with increased distance to the nearest rice filed, while the maximum growth rate was positively related with NDVI. The model shows a satisfactory performance in predicting (potentially one week in advance) mosquito

  9. Hydrological states and fluxes in terrestrial systems: from observation to prediction (John Dalton Medal Lecture)

    NASA Astrophysics Data System (ADS)

    Vereecken, Harry

    2016-04-01

    Quantification and prediction of hydrological processes requires information on the spatial and temporal distribution of soil water fluxes and soil water content. The access to spatially and temporally highly resolved soil water content and fluxes is needed to adequately test hydrological hypotheses and to validate hydrological models. In this presentation we will discuss new developments for the determination of soil water content and quantification and prediction of hydrological fluxes based on hydrogeophysical measurement techniques and novel ground- and satellite based sensing platforms. At the field scale, ground penetrating radar and passive microwave methods are presently being developed which provide the possibility to map soil water content with a high spatial and temporal resolution, also in the subsurface environment. Recent developments show that the application of full wave form inversion methods is a unique technique to derive soil water and soil hydraulic parameters from on- and off-ground systems with high spatial resolution. At the small catchment scale, wireless sensor networks are presently being developed providing soil moisture content values with a high spatial and temporal resolution. Stochastic theories have been used to interpret the relationship between average soil water content and its standard deviation. Cosmic ray sensors are presently being deployed within the TERENO observatories. These sensors provide soil moisture content values with a high temporal resolution at a scale of one to two hundred meters, thereby bridging the gap between local scale measurements and remote sensing platforms. Cosmic ray probes are extremely valuable for the determination of soil water content in agriculturally managed soils. Data assimilation methods provide a unique approach to fully exploit the value of spatially and temporally highly resolved soil water content measurements and states of the terrestrial system for the prediction of hydrological fluxes

  10. SWAT Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin

    SciTech Connect

    R. Srinivasan,; X. Zhang,; J. Arnold,

    2010-01-01

    Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing Soil and Water Assessment Tool (SWAT) input data, including hydrography, terrain, land use, soil, tile, weather, and management practices, for the Upper Mississippi River basin (UMRB). We also present a performance evaluation of SWAT hydrologic budget and crop yield simulations in the UMRB without calibration. The uncalibrated SWAT model ably predicts annual streamflow at 11 USGS gauges and crop yield at a four-digit hydrologic unit code (HUC) scale. For monthly streamflow simulation, the performance of SWAT is marginally poor compared with that of annual flow, which may be due to incomplete information about reservoirs and dams within the UMRB. Further validation shows that SWAT can predict base flow contribution ratio reasonably well. Compared with three calibrated SWAT models developed in previous studies of the entire UMRB, the uncalibrated SWAT model presented here can provide similar results. Overall, the SWAT model can provide satisfactory predictions on hydrologic budget and crop yield in the UMRB without calibration. The results emphasize the importance and prospects of using accurate spatial input data for the physically based SWAT model. This study also examines biofuel-biomass production by simulating all agricultural lands with switchgrass, producing satisfactory results in estimating biomass availability for biofuel production.

  11. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.

    2009-11-01

    Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  12. An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Petit, T.; Lavoie, A.; Boucher, M.-A.; Turcotte, R.; Fortin, V.; Anctil, F.

    2009-07-01

    Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  13. New Hydrologic Insights to Advance Geophysical Investigation of the Unsaturated Zone

    NASA Astrophysics Data System (ADS)

    Nimmo, J. R.; Perkins, K. S.

    2015-12-01

    Advances in hydrology require information from the unsaturated zone, especially for problems related to groundwater contamination, water-supply sustainability, and ecohydrology. Unsaturated-zone processes are notoriously difficult to quantify; soils and rocks are visually opaque, spatially variable in the extreme, and easily disturbed by instrument installation. Thus there is great value in noninvasive techniques that produce water-related data of high density in space and time. Methods based on resistivity and electromagnetic waves have already produced significant new understanding of percolation processes, root-zone water retention, influences of evapotranspiration on soil-water, and effects of preferential flow. Further developments are underway for such purposes as noninvasive application to greater depths, increased resolution, adaptation for lab-scale experiments, and calibration in heterogeneous media. Beyond these, however, there is need for a stronger marriage of hydrologic and geophysical knowledge and perspective. Possible means to greater and faster progress include: Apply the latest hydrologic understanding, both pore-scale and macroscopic, to the detection of preferential flow paths and their degree of activation. In the continuing advancement of hardware and techniques, draw creatively from developments in such fields as high-energy physics, medical imaging, astrogeology, high-tech semiconductors, and bioinstrumentation. Sidestep the imaging process where possible to measure essential properties and fluxes more directly. Pose questions that have a strong end-use character, like "how does storm intensity relate to aquifer recharge rate" rather than "what is the shape of the wetting front". The greatest advances in geophysical investigation of the unsaturated zone will come from methods informed by the latest understanding of unsaturated systems and processes, and aimed as directly as possible at the answers to important hydrologic questions.

  14. Advancement of Satellite-based Rainfall Applications for Hydrologic Modeling in Topographically Complex Regions

    NASA Astrophysics Data System (ADS)

    Yilmaz, Koray; Derin, Yagmur

    2014-05-01

    Accuracy and reliability of hydrological modeling studies heavily depends on quality and availability of precipitation estimates. However hydrological studies in developing countries, especially over complex topography, are limited due to unavailability and scarcity of ground-based networks. In this study we evaluate three different satellite-based rainfall retrieval algorithms namely, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), NOAA/Climate Prediction Center Morphing Method (CMORPH) and EUMETSAT's Multi-Sensor Precipitation Estimate (MPE) over orographically complex Western Black Sea Basin in Turkey, using a relatively dense rain gauge network. Our results indicated that satellite-based products significantly underestimated the rainfall in regions characterized by orographic rainfall and overestimated the rainfall in the drier regions with seasonal dependency. Further, we devised a new bias adjustment algorithm for the satellite-based rainfall products based on the "physiographic similarity" concept. Our results showed that proposed bias adjustment algorithm is better suited to regions with complex topography and provided improved results compared to the baseline "inverse distance weighting" method. To evaluate the utility of satellite-based products in hydrologic modeling studies, we implemented the MIKE SHE-MIKE 11 integrated fully distributed physically based hydrological model in the study region driven by ground-based and satellite-based precipitation estimates. Model parameter estimation was performed using a constrained calibration approach guided by multiple "signature measures" to estimate model parameters in a hydrologically meaningful way rather than using the traditional "statistical" objective functions that largely mask valuable hydrologic information during calibration process. In this presentation we will provide a discussion of evaluation and bias correction of the satellite-based precipitation products and

  15. Extending data worth methods to select multiple observations targeting specific hydrological predictions of interest

    NASA Astrophysics Data System (ADS)

    Vilhelmsen, Troels N.; Ferré, Ty P. A.

    2016-04-01

    Hydrological models are often developed to forecasting future behavior in response due to natural or human induced changes in stresses affecting hydrologic systems. Commonly, these models are conceptualized and calibrated based on existing data/information about the hydrological conditions. However, most hydrologic systems lack sufficient data to constrain models with adequate certainty to support robust decision making. Therefore, a key element of a hydrologic study is the selection of additional data to improve model performance. Given the nature of hydrologic investigations, it is not practical to select data sequentially, i.e. to choose the next observation, collect it, refine the model, and then repeat the process. Rather, for timing and financial reasons, measurement campaigns include multiple wells or sampling points. There is a growing body of literature aimed at defining the expected data worth based on existing models. However, these are almost all limited to identifying single additional observations. In this study, we present a methodology for simultaneously selecting multiple potential new observations based on their expected ability to reduce the uncertainty of the forecasts of interest. This methodology is based on linear estimates of the predictive uncertainty, and it can be used to determine the optimal combinations of measurements (location and number) established to reduce the uncertainty of multiple predictions. The outcome of the analysis is an estimate of the optimal sampling locations; the optimal number of samples; as well as a probability map showing the locations within the investigated area that are most likely to provide useful information about the forecasting of interest.

  16. Can citizen-based observations be assimilated in hydrological models to improve flood prediction?

    NASA Astrophysics Data System (ADS)

    Mazzoleni, Maurizio; Alfonso, Leonardo; Solomatine, Dimitri P.

    2015-04-01

    In the recent years, the continued technological improvement has stimulated the spread of low-cost sensors that can be used to measure hydrological variables by citizens in a more spatially distributed way than classic static physical sensors. However, such measurements have the main characteristics to have irregular arrival time and variable uncertainty. This study presents a Kalman filter based method to integrate citizen-based observations into hydrological models in order to improve flood prediction. The methodology is applied in the Brue catchment, South West of England. In order to estimate the response of the catchment to a given flood event, a lumped conceptual hydrological model is implemented. The measured precipitation values are used as perfect forecast input in the hydrological model. Synthetic streamflow values are used in this study due to the fact that citizen-based observations coming at irregular time steps are not available. The results of this study pointed out how increasing the number of uncertain citizen-based observations within two model time steps can improve the model accuracy leading to a better flood forecast. Therefore, observations uncertainty influences the model accuracy more than the irregular moments in which the streamflow observations are assimilated into the hydrological model. This study is part of the FP7 European Project WeSenseIt Citizen Water Observatory (http://wesenseit.eu/).

  17. Unraveling the hydrology of a Himalayan catchment through integration of high resolution in situ data and remote sensing with an advanced simulation model

    NASA Astrophysics Data System (ADS)

    Ragettli, S.; Pellicciotti, F.; Immerzeel, W. W.; Miles, E. S.; Petersen, L.; Heynen, M.; Shea, J. M.; Stumm, D.; Joshi, S.; Shrestha, A.

    2015-04-01

    The hydrology of high-elevation watersheds of the Hindu Kush-Himalaya region (HKH) is poorly known. The correct representation of internal states and process dynamics in glacio-hydrological models can often not be verified due to missing in situ measurements. We use a new set of detailed ground data from the upper Langtang valley in Nepal to systematically guide a state-of-the art glacio-hydrological model through a parameter assigning process with the aim to understand the hydrology of the catchment and contribution of snow and ice processes to runoff. 14 parameters are directly calculated on the basis of local data, and 13 parameters are calibrated against 5 different datasets of in situ or remote sensing data. Spatial fields of debris thickness are reconstructed through a novel approach that employs data from an Unmanned Aerial Vehicle (UAV), energy balance modeling and statistical techniques. The model is validated against measured catchment runoff (Nash-Sutcliffe efficiency 0.87) and modeled snow cover is compared to Landsat snow cover. The advanced representation of processes allowed assessing the role played by avalanching for runoff for the first time for a Himalayan catchment (5% of annual water inputs to the hydrological system are due to snow redistribution) and to quantify the hydrological significance of sub-debris ice melt (9% of annual water inputs). Snowmelt is the most important contributor to total runoff during the hydrological year 2012/2013 (representing 40% of all sources), followed by rainfall (34%) and ice melt (26%). A sensitivity analysis is used to assess the efficiency of the monitoring network and identify the timing and location of field measurements that constrain model uncertainty. The methodology to set up a glacio-hydrological model in high-elevation regions presented in this study can be regarded as a benchmark for modelers in the HKH seeking to evaluate their calibration approach, their experimental setup and thus to reduce the

  18. Program plan and summary, remote fluvial experimental (REFLEX) series: Research experiments using advanced remote sensing technologies with emphasis on hydrologic transport, and hydrologic-ecologic interactions

    SciTech Connect

    Wobber, F.J.

    1986-10-01

    This document describes research designed to evaluate advanced remote sensing technologies for environmental research. A series of Remote Fluvial Experiments (REFLEX) - stressing new applications of remote sensing systems and use of advanced digital analysis methods - are described. Program strategy, experiments, research areas, and future initiatives are summarized. The goals of REFLEX are: (1) to apply new and developing aerial and satellite remote sensing technologies - including both advanced sensor systems and digital/optical processing - for interdisciplinary scientific experiments in hydrology and to hydrologic/ecologic interactions; (2) to develop new concepts for processing and analyzing remote sensing data for general scientific application; and (3) to demonstrate innovative analytical technologies that advance the state of the art in applying information from remote sensing systems, for example, supercomputer processing and analysis.

  19. Predicting Hydrological Drought: Relative Contributions of Soil Moisture and Snow Information to Seasonal Streamflow Prediction Skill

    NASA Technical Reports Server (NTRS)

    Koster, R.; Mahanama, S.; Livneh, B.; Lettenmaier, D.; Reichle, R.

    2011-01-01

    in this study we examine how knowledge of mid-winter snow accumulation and soil moisture conditions contribute to our ability to predict streamflow months in advance. A first "synthetic truth" analysis focuses on a series of numerical experiments with multiple sophisticated land surface models driven with a dataset of observations-based meteorological forcing spanning multiple decades and covering the continental United States. Snowpack information by itself obviously contributes to the skill attained in streamflow prediction, particularly in the mountainous west. The isolated contribution of soil moisture information, however, is found to be large and significant in many areas, particularly in the west but also in region surrounding the Great Lakes. The results are supported by a supplemental, observations-based analysis using (naturalized) March-July streamflow measurements covering much of the western U.S. Additional forecast experiments using start dates that span the year indicate a strong seasonality in the skill contributions; soil moisture information, for example, contributes to kill at much longer leads for forecasts issued in winter than for those issued in summer.

  20. A simple hydrologic model for rapid prediction of runoff from ungauged coastal catchments

    NASA Astrophysics Data System (ADS)

    Wan, Yongshan; Konyha, Kenneth

    2015-09-01

    We developed a lumped conceptual rainfall-runoff model for rapid prediction of runoff generated in the unique hydrological setting with flat terrain, sandy soils, high groundwater table, and a dense drainage canal network in south Florida. The model is conceptualized as rainfall and evapotranspiration filling and emptying the root zone and excess rainfall recharging three storage zones. Outflows from these storage zones, routed with parallel arrangement of three linear reservoirs, represent different flow components of catchment runoff, i.e., slow drainage (shallow subsurface flow), medium drainage (interflow and saturation excess overland flow), and fast drainage (direct runoff from impervious urban areas or from water table management in agricultural land). The model is parsimonious with eight model parameters along with two optional water management parameters. A regionalization study was conducted through model parameterization to achieve target hydrological behavior of typical land uses, which are the most significant basin descriptor affecting catchment hydrology in south Florida. Cross validation with 16 gauged basins dominated by urban, agricultural, and natural lands, respectively, indicated that the model provides an effective tool for rapid prediction of runoff in ungauged basins using the regionalized model parameters. A case study is presented, involving application of the model to support real-time adaptive management to hydrological operations for protection of estuarine ecosystems.

  1. Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions

    NASA Astrophysics Data System (ADS)

    Rozalis, Shahar; Morin, Efrat; Yair, Yoav; Price, Colin

    2010-11-01

    SummaryFlash floods cause some of the most severe natural disasters in Europe but Mediterranean areas are especially vulnerable. They can cause devastating damage to property, infrastructures and loss of human life. The complexity of flash flood generation processes and their dependency on different factors related to watershed properties and rainfall characteristics make flash flood prediction a difficult task. In this study, as part of the EU-FLASH project, we used an uncalibrated hydrological model to simulate flow events in a 27 km2 Mediterranean watershed in Israel to analyze and better understand the various factors influencing flows. The model is based on the well-known SCS curve number method for rainfall-runoff calculations and on the kinematic wave method for flow routing. Existing data available from maps, GIS and field studies were used to define model parameters, and no further calibration was conducted to obtain a better fit between computed and observed flow data. The model rainfall input was obtained from the high temporal and spatial resolution radar data adjusted to rain gauges. Twenty flow events that occurred within the study area over a 15 year period were analyzed. The model shows a generally good capability in predicting flash flood peak discharge in terms of their general level, classified as low, medium or high (all high level events were correctly predicted). It was found that the model mainly well predicts flash floods generated by intense, short-lived convective storm events while model performances for low and moderate flows generated by more widespread winter storms were quite poor. The degree of urban development was found to have a large impact on runoff amount and peak discharge, with higher sensitivity of moderate and low flow events relative to high flows. Flash flood generation was also found to be very sensitive to the temporal distribution of rain intensity within a specific storm event.

  2. Evaluation of real-time hydrometeorological ensemble prediction on hydrologic scales in Northern California

    NASA Astrophysics Data System (ADS)

    Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.

    2014-11-01

    The paper presents an evaluation of real time ensemble forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically downscale operational multi-lead ensemble forecasts from the Global Ensemble Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide ensemble reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the ensemble prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to downscale the NCEP CFS ensemble forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the ensemble predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast ensemble. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season

  3. Knowledge Discovery in Hydrologic Data: a Framework for Simulation and Prediction

    NASA Astrophysics Data System (ADS)

    Khalil, A. F.

    2004-12-01

    Uncertainty, non-stationarity, noise, and paucity of data all limit the prediction capabilities of hydrologic models. In this paper, we adopt a Bayesian predictive approach for forecasting that combines the features of excellent generalization properties and sparse representation. There are three novelties in the resulting framework: first, the uncertainty in model parameters is incorporated in the prediction; second, a multi-objective optimization algorithm is employed to account for the uncertainty in model structure (i.e., optimal model selection); and third, this framework allows AƒAøAøâ_sA¬A<Å"detection of shiftsAƒAøAøâ_sA¬Aøâ_zAø in the sense that, as we observe the behavior of a process through time, our framework detects drift (e.g., changes in the hydrologic processes caused by climatic changes) and thence initiates adaptations in model structure in response to a recognized shift in the underlying processes. Finally, given knowledge of some state and exogenous conditions, the framework is applied in an on-line fashion to provide probabilistic forecasts of future system states. For many hydrologic systems of practical interest, these forecasts can be accomplished in real-time and can provide valuable management information.

  4. Regionalized Hydrologic Parameters Estimates for a Seamless Prediction of Continental scale Water Fluxes and States

    NASA Astrophysics Data System (ADS)

    Kumar, R.; Mai, J.; Rakovec, O.; Cuntz, M.; Thober, S.; Zink, M.; Attinger, S.; Schaefer, D.; Schrön, M.; Samaniego, L. E.

    2015-12-01

    Accurate representation of water fluxes and states is crucial for hydrological assessments of societally relevant events such as floods and droughts. Hydrologic and/or land surface models are now commonly used for this purpose. The seamless prediction of continental scale water fluxes from these models requires among other things (i) a robust parameterization technique that allows the model to operate across a range of spatial resolutions and (ii) an efficient parameter estimation technique to derive a representative set of spatially consistent hydrologic parameters to avoid discontinuities of simulated hydrologic fields. In this study, we demonstrate the applicability of a mesoscale hydrologic modeling framework that incorporates a novel multiscale parameter regionalization technique (mHM-MPR) to derive the long-term gridded estimates of water fluxes and states over the Pan-EU domain. The MPR technique allows establishing linkages between hydrologic parameter fields and basin geophysical attributes (e.g., terrain, soil, vegetation properties) through a set of transfer functions and quasi-scale invariant global parameters. We devise a multi-basin parameter estimation strategy that utilizes observed streamflows from a reduced set of hydrologically diverse basins to infer a representative set of global parameters. The selection of diverse basins is guided through a stepwise clustering algorithm based on the basins geophysical and hydro-climatic attributes. Results of this strategy are contrasted against the single-basin calibration strategy across 400 European basins varying from approximately 100 km2 to 500000 km2. The single-basin parameter estimates although produced the site-specific best results, but their transferability to other basins resulted in poor performance. Initial results indicate that the multi-basin calibration strategy is at least as good as the best single-basin cross-validated results. Furthermore, the gridded fields of hydrologic parameters and

  5. Performance evaluation of merged satellite rainfall products based on spatial and seasonal signatures of hydrologic predictability

    NASA Astrophysics Data System (ADS)

    Gebregiorgis, Abebe; Hossain, Faisal

    2013-10-01

    Despite the inherent estimation uncertainty, remote sensing based rainfall data have enormous value for stream flow simulation. Recent investigations have shown that the historical performance of satellite products in hydrologic prediction can be a useful (diagnostic) proxy for merging products to a more superior performing state for prognostic simulations (i.e., forward in time). Using a hydrologic model set-up over the entire Mississippi River Basin (MRB) and three widely used satellite rainfall products (3B42RT, CMORPH and PERSIANN-CCS), this study explored a merging scheme based on runoff predictability. The spatial and temporal signatures of variability were closely investigated to understand the impact on prediction skill of the merging scheme. The spatial variability (i.e., non-uniform) considered the grid box by grid box variation at the native resolution of individual satellite products, while the temporal variability (i.e., non-stationary) was confined to variation in 3 month-long seasons (winter, spring, summer and fall). When both the spatial and temporal variability in runoff predictability was leveraged, the merging scheme yielded the largest improvement over individual product's performance forward in time. During an independent validation assessment, the stream flow simulated by the merged product was more strongly correlated with observed discharge (than individual products) at 12 gauging stations. In terms of reduction in root mean squared error (RMSE), the merged product showed an improvement of 57% for 3B42RT, 63% for CMORPH and 68% for PERSIANN-CCS products. The investigation clearly showed that any ‘operational’ and hydrologic predictability-based merging scheme for unifying available satellite rainfall products must factor in both the spatial and temporal signatures of runoff predictability to achieve consistently more superior prognostic skill.

  6. Weather Prediction Improvement Using Advanced Satellite Technology

    NASA Technical Reports Server (NTRS)

    Einaudi, Franco; Uccellini, L.; Purdom, J.; Rogers, D.; Gelaro, R.; Dodge, J.; Atlas, R.; Lord, S.

    2001-01-01

    We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common

  7. Predicting glacio-hydrologic change in the headwaters of the Zongo River, Cordillera Real, Bolivia

    NASA Astrophysics Data System (ADS)

    Frans, Chris; Istanbulluoglu, Erkan; Lettenmaier, Dennis P.; Naz, Bibi S.; Clarke, Garry K. C.; Condom, Thomas; Burns, Pat; Nolin, Anne W.

    2015-11-01

    In many partially glacierized watersheds glacier recession driven by a warming climate could lead to complex patterns of streamflow response over time, often marked with rapid increases followed by sharp declines, depending on initial glacier ice cover and rate of climate change. Capturing such "phases" of hydrologic response is critical in regions where communities rely on glacier meltwater, particularly during low flows. In this paper, we investigate glacio-hydrologic response in the headwaters of the Zongo River, Bolivia, under climate change using a distributed glacio-hydrological model over the period of 1987-2100. Model predictions are evaluated through comparisons with satellite-derived glacier extent estimates, glacier surface velocity, in situ glacier mass balance, surface energy flux, and stream discharge measurements. Historically (1987-2010) modeled glacier melt accounts for 27% of annual runoff, and 61% of dry season (JJA) runoff on average. During this period the relative glacier cover was observed to decline from 35 to 21% of the watershed. In the future, annual and dry season discharge is projected to decrease by 4% and 27% by midcentury and 25% and 57% by the end of the century, respectively, following the loss of 81% of the ice in the watershed. Modeled runoff patterns evolve through the interplay of positive and negative trends in glacier melt and increased evapotranspiration as the climate warms. Sensitivity analyses demonstrate that the selection of model surface energy balance parameters greatly influences the trajectory of hydrological change projected during the first half of the 21st century. These model results underscore the importance of coupled glacio-hydrology modeling.

  8. Predicting glacio-hydrologic change in the headwaters of the Zongo River, Cordillera Real, Bolivia

    DOE PAGESBeta

    Frans, Chris; Istanbulluoglu, Erkan; Lettenmaier, Dennis; Naz, Bibi S.; Clarke, Garry K. C.; Condom, Thomas; Burns, Pat; Nolin, Anne W.

    2015-11-19

    In many partially glacierized watersheds glacier recession driven by a warming climate could lead to complex patterns of streamflow response over time, often marked with rapid increases followed by sharp declines, depending on initial glacier ice cover and rate of climate change. Capturing such "phases'' of hydrologic response is critical in regions where communities rely on glacier meltwater, particularly during low flows. In this study, we investigate glacio-hydrologic response in the headwaters of the Zongo River, Bolivia, under climate change using a distributed glacio-hydrological model over the period of 1987-2100. Model predictions are evaluated through comparisons with satellite-derived glacier extentmore » estimates, glacier surface velocity, in situ glacier mass balance, surface energy flux, and stream discharge measurements. Historically (1987-2010) modeled glacier melt accounts for 27% of annual runoff, and 61% of dry season (JJA) runoff on average. During this period the relative glacier cover was observed to decline from 35 to 21% of the watershed. In the future, annual and dry season discharge is projected to decrease by 4% and 27% by midcentury and 25% and 57% by the end of the century, respectively, following the loss of 81% of the ice in the watershed. Modeled runoff patterns evolve through the interplay of positive and negative trends in glacier melt and increased evapotranspiration as the climate warms. Sensitivity analyses demonstrate that the selection of model surface energy balance parameters greatly influences the trajectory of hydrological change projected during the first half of the 21st century. In conclusion, these model results underscore the importance of coupled glacio-hydrology modeling.« less

  9. Predicting glacio-hydrologic change in the headwaters of the Zongo River, Cordillera Real, Bolivia

    SciTech Connect

    Frans, Chris; Istanbulluoglu, Erkan; Lettenmaier, Dennis; Naz, Bibi S.; Clarke, Garry K. C.; Condom, Thomas; Burns, Pat; Nolin, Anne W.

    2015-11-19

    In many partially glacierized watersheds glacier recession driven by a warming climate could lead to complex patterns of streamflow response over time, often marked with rapid increases followed by sharp declines, depending on initial glacier ice cover and rate of climate change. Capturing such "phases'' of hydrologic response is critical in regions where communities rely on glacier meltwater, particularly during low flows. In this study, we investigate glacio-hydrologic response in the headwaters of the Zongo River, Bolivia, under climate change using a distributed glacio-hydrological model over the period of 1987-2100. Model predictions are evaluated through comparisons with satellite-derived glacier extent estimates, glacier surface velocity, in situ glacier mass balance, surface energy flux, and stream discharge measurements. Historically (1987-2010) modeled glacier melt accounts for 27% of annual runoff, and 61% of dry season (JJA) runoff on average. During this period the relative glacier cover was observed to decline from 35 to 21% of the watershed. In the future, annual and dry season discharge is projected to decrease by 4% and 27% by midcentury and 25% and 57% by the end of the century, respectively, following the loss of 81% of the ice in the watershed. Modeled runoff patterns evolve through the interplay of positive and negative trends in glacier melt and increased evapotranspiration as the climate warms. Sensitivity analyses demonstrate that the selection of model surface energy balance parameters greatly influences the trajectory of hydrological change projected during the first half of the 21st century. In conclusion, these model results underscore the importance of coupled glacio-hydrology modeling.

  10. Towards a Predictive Theory of Malaria: Connections to Spatio-temporal Variability of Climate and Hydrology

    NASA Astrophysics Data System (ADS)

    Endo, N.; Eltahir, E. A. B.

    2015-12-01

    Malaria transmission is closely linked to climatology, hydrology, environment, and the biology of local vectors. These factors interact with each other and non-linearly influence malaria transmission dynamics, making prediction and prevention challenging. Our work attempts to find a universality in the multi-dimensional system of malaria transmission and to develop a theory to predict emergence of malaria given a limited set of environmental and biological inputs.A credible malaria transmission dynamics model, HYDREMATS (Bomblies et al., 2008), was used under hypothetical settings to investigate the role of spatial and temporal distribution of vector breeding pools. HYDREMATS is a mechanistic model and capable of simulating the basic reproduction rate (Ro) without bold assumptions even under dynamic conditions. The spatial distribution of pools is mainly governed by hydrological factors; the impact of pool persistence and rainy season length on malaria transmission were investigated. Also analyzed was the impact of the temporal distribution of pools relative to human houses. We developed non-dimensional variables combining the hydrological and biological parameters. Simulated values of Ro from HYDREMATS are presented in a newly-introduced non-dimensional plane, which leads to a some-what universal theory describing the condition for sustainable malaria transmission. The findings were tested against observations both from the West Africa and the Ethiopian Highland, representing diverse hydroclimatological conditions. Predicated Ro values from the theory over the two regions are in good agreement with the observed malaria transmission data.

  11. Improved methodology for temperature predictions in advanced reactors

    SciTech Connect

    Ambrosek, R.G.; Chang, G.S.

    1995-10-01

    Advanced nuclear reactors maximize power and/or flux levels for increased performance levels. One of the challenges is accurate prediction of temperatures in the structural components and experiments. An improved methodology utilizing the computer codes MCNP and ABAQUS has been demonstrated in instrumented experiments at the Advanced Test Reactor. The analytical predictions have shown excellent agreement with the measured results.

  12. Evaluating hydrological ensemble predictions using a large and varied set of catchments (Invited)

    NASA Astrophysics Data System (ADS)

    Ramos, M.; Andreassian, V.; Perrin, C.; Loumagne, C.

    2010-12-01

    It is widely accepted that local and national operational early warning systems can play a key role in mitigating flood damage and losses to society while improving risk awareness and flood preparedness. Over the last years, special attention has been paid to efficiently couple meteorological and hydrological warning systems to track uncertainty and achieve longer lead times in hydrological forecasting. Several national and international scientific programs have focused on the pre-operational test and development of ensemble hydrological forecasting. Based on the lumped soil-moisture-accounting type rainfall-runoff model GRP, developed at Cemagref, we have set up a research tool for ensemble forecasting and conducted several studies to evaluate the quality of streamflow forecasts. The model has been driven by available archives of weather ensemble prediction systems from different sources (Météo-France, ECMWF, TIGGE archive). Our approach has sought to combine overall validation under varied geographical and climate conditions (to assess model robustness and generality) and site-specific validation (to locally accept or reject the hydrologic forecast system and contribute to defining its limits of applicability). The general aim is to contribute to methodological developments concerning a wide range of key aspects in hydrological forecasting, including: the links between predictability skill and catchment characteristics, the magnitude and the distribution of forecasting errors, the analysis of nested or neighbouring catchments for prediction in ungauged basins, as well as the reliability of model predictions when forecasting under conditions not previously encountered during the period of setup and calibration of the system. This presentation will cover the aforementioned topics and present examples from studies carried out to evaluate and inter-compare ensemble forecasting systems using a large and varied set of catchments in France. The specific need to

  13. Can ENSO teleconnections be exploited for seasonal hydrological prediction in the Western U.S.

    NASA Astrophysics Data System (ADS)

    Lettenmaier, Dennis

    2016-04-01

    El Nino is perhaps the strongest teleconnection in the extratropics. The current El Nino event is thought to be the strongest in the instrumental record. We use a set of ten grouped climate and snow water equivalent (SWE) stations distributed across the western U.S. to evaluate how well the U.S. National Multimodel Ensemble (NMME) seasonal climate forecast system is able to exploit ENSO signals. We do so through a comparison of stratified Ensemble Hydrologic Prediction (ESP), in which hydrological forcings for the VIC model are subselected from prior El Nino years, with NMME seasonal forecasts for the same years. We also evaluate the skill of both methods for point forecasts of SWE and accumulated winter (Nov-Mar) precipitation and average winter temperature for winter 2015-2016, and for SWE at the 10 forecast points for Apr 1, 2016.

  14. Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil

    NASA Technical Reports Server (NTRS)

    Arya, L. M. (Principal Investigator)

    1980-01-01

    Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.

  15. Use of different sampling schemes in machine learning-based prediction of hydrological models' uncertainty

    NASA Astrophysics Data System (ADS)

    Kayastha, Nagendra; Solomatine, Dimitri; Lal Shrestha, Durga; van Griensven, Ann

    2013-04-01

    In recent years, a lot of attention in the hydrologic literature is given to model parameter uncertainty analysis. The robustness estimation of uncertainty depends on the efficiency of sampling method used to generate the best fit responses (outputs) and on ease of use. This paper aims to investigate: (1) how sampling strategies effect the uncertainty estimations of hydrological models, (2) how to use this information in machine learning predictors of models uncertainty. Sampling of parameters may employ various algorithms. We compared seven different algorithms namely, Monte Carlo (MC) simulation, generalized likelihood uncertainty estimation (GLUE), Markov chain Monte Carlo (MCMC), shuffled complex evolution metropolis algorithm (SCEMUA), differential evolution adaptive metropolis (DREAM), partical swarm optimization (PSO) and adaptive cluster covering (ACCO) [1]. These methods were applied to estimate uncertainty of streamflow simulation using conceptual model HBV and Semi-distributed hydrological model SWAT. Nzoia catchment in West Kenya is considered as the case study. The results are compared and analysed based on the shape of the posterior distribution of parameters, uncertainty results on model outputs. The MLUE method [2] uses results of Monte Carlo sampling (or any other sampling shceme) to build a machine learning (regression) model U able to predict uncertainty (quantiles of pdf) of a hydrological model H outputs. Inputs to these models are specially identified representative variables (past events precipitation and flows). The trained machine learning models are then employed to predict the model output uncertainty which is specific for the new input data. The problem here is that different sampling algorithms result in different data sets used to train such a model U, which leads to several models (and there is no clear evidence which model is the best since there is no basis for comparison). A solution could be to form a committee of all models U and

  16. The utility of satellite precipitation products for hydrologic prediction in topographically complex regions: The Chehalis River Basin, WA as a case study

    NASA Astrophysics Data System (ADS)

    Cao, Q.; Mehran, A.; Lettenmaier, D. P.; Mass, C.; Johnson, N.

    2015-12-01

    Accurate measurements of precipitation are of great importance in hydrologic predictions especially for floods, which are a pervasive natural hazard. One of the primary objectives of Global Precipitation Measurement (GPM) mission is to provide a basis for hydrologic predictions using satellite sensors. A major advance in GPM relative to the Tropical Rainfall Measuring Mission (TRMM) is that it observes atmospheric river (AR) events, most of which have landfall too far north to be tracked by TRMM. These events are responsible for most major floods along the U.S. West Coast. We address the question of whether, for hydrologic modeling purposes, it is better to use precipitation products derived directly from GPM and/or other precipitation fields from weather models that have assimilated satellite data. Our overall strategy is to compare different methods for prediction of flood and/or high flow events by different forcings on the hydrologic model. We examine four different configurations of the Distroibute Hydrology Soil Vegetation Model (DHSVM) over the Chehalis River Basin that use a) precipitation forcings based on gridded station data; b) precipitation forcings based on NWS WSR-88D data, c) forcings based from short-term precipitation forecasts using the Weather Research and Forecasting (WRF) mesoscale atmospheric model, and d) satellite-based precipitation estimates (TMPA and IMERG). We find that in general, biases in the radar and satellite products result in much larger errors than with either gridded station data or WRF forcings, but if these biases are removed, comparable performance in flood predictions can be achieved by Satellite-based precipitation estimates (TMPA and IMERG).

  17. Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

    NASA Astrophysics Data System (ADS)

    Dogulu, N.; López López, P.; Solomatine, D. P.; Weerts, A. H.; Shrestha, D. L.

    2015-07-01

    In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

  18. Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

    NASA Astrophysics Data System (ADS)

    Snedden, Gregg A.; Steyer, Gregory D.

    2013-02-01

    Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

  19. Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

    USGS Publications Warehouse

    Snedden, Gregg A.; Steyer, Gregory D.

    2013-01-01

    Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

  20. Recent Advances in Predictive (Machine) Learning

    SciTech Connect

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  1. Impact of time-variable vegetation on accuracy of rapid hydrologic predictions

    NASA Astrophysics Data System (ADS)

    Stec, Magdalena; Niedzielski, Tomasz

    2016-04-01

    It is crucial to identify the processes that impact errors of hydrologic forecasts. Since existence of vegetation and its ability to store precipitation is an important element of water distribution in the catchment, especially at the beginning of a rainfall event, it may be considered as one of the processes influencing skills of hydrological forecasts. The main objective of the study is to verify the hypothesis that water level predictions are controlled by vegetation dynamics in the contributing mountainous basins. The analysis is conducted for the upper Nysa Klodzka catchment with the outlet in Bardo (SW Poland). The basin includes a mid-mountain abasement covered with crops, while surrounding medium-altitude mountain ranges are mainly covered with forests. We focus on the entire year, from autumn 2013 to summer 2014. Herein, we analyze prediction errors and efficiency measures of hydrologic forecasts provided by two stochastic models - uni- and multivariate autoregressive models as well as their two-model ensemble prediction. In addition, we use the satellite-derived Leaf Area Index (LAI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS). Hydrological prognoses are derived by the HydroProg real-time rapid forecasting system, built at the University of Wroclaw (Poland) in frame of the research project 2011/01/D/ST10/04171 of the National Science Centre of Poland. Correlation analysis between the plant maximum water storage capacity and prediction error/skill statistics (mean absolute error, root mean square error, Nash-Sutcliffe efficiency , index of agreement) is conducted. To cope with small sample size, the bootstrap simulation is performed. We conclude that there is a strong negative association between mean or median prediction errors and vegetation state for all meteorological seasons of a year. This result implies that basins with higher interception potential are more vulnerable to forecast inaccuracy than those with sparse natural

  2. Combining meteorological ensemble prediction, data assimilation and hydrological multimodel to reduce and untangle sources of uncertainty

    NASA Astrophysics Data System (ADS)

    Thiboult, Antoine; Anctil, François; Boucher, Marie-Amélie

    2015-04-01

    Hydrological ensemble prediction systems offer the possibility to dynamically assess forecast uncertainty. An ensemble may be issued wherever the uncertainty is situated along the meteorological chain. We commonly identify three main sources of uncertainty: meteorological forcing, hydrological initial conditions, and structural and parameter uncertainty. To address these uncertainties, different techniques have been developed. Meteorological ensemble prediction systems gained in popularity among researchers and operational forecasters as it allows to account for forcing uncertainties. Many data assimilation techniques have been applied to hydrology to reinitialize model states in order to issue more accurate and sharper predictive density functions. At last, multimodel simulation allows to get away from the quest of single best parameter and structure pitfall. The knowledge about these individual techniques is getting extensive and many individual applications can be found in the literature. Even though they proved to improve upon traditional forecasting, they frequently fail to issue fully reliable hydrological forecast as all sources of uncertainty are not tackled. Therefore, an improvement can be obtained in combining them, as it provides a more comprehensive handling of errors. Moreover, using these techniques separately or in combination allows to issue more reliable forecasts but also to identify explicitly the amount of total uncertainty that each technique accounts for. At the end, these sources of error can be characterized in terms of magnitude and lead time influence. As these techniques are frequently used alone, they are usually tuned to perform individually. To reach optimal performance, they should be set jointly. Among them, the data assimilation technique offers a large flexibility in its setting and therefore requires a proper setting considering the other ensemble techniques used. This question is also raised for the hydrological model selection

  3. Advanced technology wind shear prediction system evaluation

    NASA Technical Reports Server (NTRS)

    Gering, Greg

    1992-01-01

    The program overviews: (1) American Airline (AA)/Turbulence Prediction Systems (TPS), which have installed forward looking infrared predictive windshear system on 3 MD-80 aircraft; (2) AA/TPS AWAS III evaluation, which is a joint effort and is installed in the noise landing gear (NLG) area and a data recorder installed in the E/E compartment.

  4. Extension and Application of Feature Prediction Model for Synthesis of Hydrologic Records

    NASA Astrophysics Data System (ADS)

    Panu, Umed Singh; Unny, T. E.

    1980-02-01

    The method described in this paper for the synthesis of streamflows differs from the traditional approaches in synthetic hydrology in the sense that it utilizes the information contained in or among the groups of data in a streamflow record. The existense of such groups in geophysical records, including hydrologic records, is well emphasized by Hurst (1951). Further, in the proposed method, based on concepts of pattern recognition, neither a basic structure nor any preconceived model is imposed on the data; rather the data are allowed to speak for themselves in a most `democratic' way. The preliminary details of the method were provided in an earlier paper by Panu et al. (1978). The intent of this paper is to describe a procedure whereby it is possible to specify explicitly multivariate probability distribution for the intrapattern structure and first-order Markovian dependence for the interpattern structure in the feature prediction model (Panu et al., 1978). The various steps involved in the construction and operation of the model for streamflow synthesis are presented. The application of the model for synthesizing monthly streamflow records of three Canadian rivers exhibiting biannual cycles is explained. Statistical and hydrological tests show that these synthetic realizations possess relevant properties that are comparable with the corresponding properties contained in the historical record. This article should be read in conjunction with the previous publication by Panu et al. (1978).

  5. A radar-based hydrological model for flash flood prediction in the dry regions of Israel

    NASA Astrophysics Data System (ADS)

    Ronen, Alon; Peleg, Nadav; Morin, Efrat

    2014-05-01

    Flash floods are floods which follow shortly after rainfall events, and are among the most destructive natural disasters that strike people and infrastructures in humid and arid regions alike. Using a hydrological model for the prediction of flash floods in gauged and ungauged basins can help mitigate the risk and damage they cause. The sparsity of rain gauges in arid regions requires the use of radar measurements in order to get reliable quantitative precipitation estimations (QPE). While many hydrological models use radar data, only a handful do so in dry climate. This research presents a robust radar-based hydro-meteorological model built specifically for dry climate. Using this model we examine the governing factors of flash floods in the arid and semi-arid regions of Israel in particular and in dry regions in general. The hydrological model built is a semi-distributed, physically-based model, which represents the main hydrological processes in the area, namely infiltration, flow routing and transmission losses. Three infiltration functions were examined - Initial & Constant, SCS-CN and Green&Ampt. The parameters for each function were found by calibration based on 53 flood events in three catchments, and validation was performed using 55 flood events in six catchments. QPE were obtained from a C-band weather radar and adjusted using a weighted multiple regression method based on a rain gauge network. Antecedent moisture conditions were calculated using a daily recharge assessment model (DREAM). We found that the SCS-CN infiltration function performed better than the other two, with reasonable agreement between calculated and measured peak discharge. Effects of storm characteristics were studied using synthetic storms from a high resolution weather generator (HiReS-WG), and showed a strong correlation between storm speed, storm direction and rain depth over desert soils to flood volume and peak discharge.

  6. From Boundary Layer Turbulence to Hydrologic Response: Recent Results on Scaling, Nonlinearity, and Predictability and Their Implications

    NASA Astrophysics Data System (ADS)

    Foufoula-Georgiou, E.

    2002-12-01

    Deepening our understanding of the space-time variability of atmospheric/hydrologic processes and their interactions over a range of scales has important implications for improving model parameterizations and increasing the accuracy of predictive models. At the same time, the inherent nonlinear and chaotic character of some of these processes imposes limits on their predictability, and therefore provides upper bounds on the expected prediction accuracy from numerical models. This paper will address questions of scaling, nonlinearity and predictability in processes active at two major interfaces of the hydrologic system: the land-atmosphere interface, and the land-water interface. Specifically, recent findings and their practical implications will be presented on: (a) multiscale interactions in turbulent boundary layers and implications for boundary condition formulations; (b) predictability assessment of turbulent velocities in a boundary layer as a function of scale; and (c) nonlinear dynamics of basin hydrologic response as a function of spatio-temporally varying forcing and basin geomorphological organization.

  7. Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

    USGS Publications Warehouse

    Anning, David W.; Parker, John T.C.

    2009-01-01

    Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold

  8. Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments

    NASA Astrophysics Data System (ADS)

    Dogulu, N.; López López, P.; Solomatine, D. P.; Weerts, A. H.; Shrestha, D. L.

    2014-09-01

    In operational hydrology, estimation of predictive uncertainty of hydrological models used for flood modelling is essential for risk based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analyzing and predicting uncertainty. However, case studies comparing performance of these methods, most particularly predictive uncertainty methods, are limited. This paper focuses on two predictive uncertainty methods that differ in their methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC), aiming at identifying possible advantages and disadvantages of these methods (both estimating residual uncertainty) based on their comparative performance. We test these two methods on several catchments (from UK) that vary in its hydrological characteristics and models. Special attention is given to the errors for high flow/water level conditions. Furthermore, normality of model residuals is discussed in view of clustering approach employed within the framework of UNEEC method. It is found that basin lag time and forecast lead time have great impact on quantification of uncertainty (in the form of two quantiles) and achievement of normality in model residuals' distribution. In general, uncertainty analysis results from different case studies indicate that both methods give similar results. However, it is also shown that UNEEC method provides better performance than QR for small catchments with changing hydrological dynamics, i.e. rapid response catchments. We recommend that more case studies of catchments from regions of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features be tested.

  9. Predicting success on the Advanced Placement Biology Examination

    NASA Astrophysics Data System (ADS)

    Shepherd, Lesa Hanlin

    Four hundred sixty students in four public high schools were used as subjects to determine which of eleven academic and demographic factors studied were significant predictors of success for the Advanced Placement Biology Examination. Factors studied were attendance, class rank, gender, grade level at the time of the examination, grade point average, level of prerequisite biology course, number of Advanced Placement Examinations taken in the year prior to the Advanced Placement Biology Examination, number of Advanced Placement Examinations taken in the same year as the Advanced Placement Biology Examination, proposed major in college, race, and SAT mathematics, verbal, and combined score. Significant relationships were found to exist between the Advanced Placement Biology Examination score and attendance, class rank, gender, grade level at the time of the Advanced Placement Biology Examination, grade point average, number of Advanced Placement Examinations taken in the year prior to the Advanced Placement Biology Examination, number of Advanced Placement Examinations taken in the same year as the Advanced Placement Biology Examination, race, and SAT scores. Significant relationships were not found to exist between Advanced Placement Biology Examination score and level prerequisite biology course and Advanced Placement Biology Examination score and proposed major in college. A multiple regression showed the best combination of predictors to be attendance, SAT verbal score, and SAT mathematics score. Discriminant analysis showed the variables in this study to be good predictors of whether the student would pass the Advanced Placement Biology Examination (score a 3, 4, or 5) or fail the Advanced Placement Biology Examination (score a 1 or 2). These results demonstrated that significant predictors for the Advanced Placement Biology Examination do exist and can be used to assist in the prediction of scores, prediction of passing or failing, the identification of

  10. Predicting current and future peatmoss drought stress: Impact of hydrological complexity

    NASA Astrophysics Data System (ADS)

    Nijp, Jelmer; Metselaar, Klaas; Limpens, Juul; Teutschbein, Claudia; Peichl, Matthias; Nilsson, Mats; Berendse, Frank; van der Zee, Sjoerd

    2016-04-01

    Northern peatlands sequester enormous amounts of carbon and therefore represent a carbon store of global importance. The vegetation in northern peatlands is dominated by peat-forming bryophytes of the genus Sphagnum. The growth of this carbon fixer, and hence its carbon uptake, strongly depends on the moisture availability in the living moss layer, which is a function of both water table and rewetting by rain. Peatland hydrology models are used to predict how changes in climate may modify the future water balance of peatmoss carpets and influence associated carbon and energy balances. These models, however, differ considerably in the number and type of processes included, which will have yet unknown consequences for peatland drought predictions in a future climate. Here, we assessed the importance of rainwater storage and peat volume change for predicting peatmoss drought projections in northern peatlands using an ensemble of downscaled, bias-corrected climate scenarios for current (1991 - 2020) and future (2061 - 2090) climate. Peatmoss drought projections were compared among four model variants with or without rainwater storage in the peatmoss carpet and peat volume change, which are considered as two important hydrological feedbacks controlling moss moisture availability. The performance of the four model variants was assessed using field data from a site in northern Sweden (Degerö Stormyr, 64°N 19°E). Our results show that adding rainwater storage in the moss layer as well as peat volume change significantly improved model performance; the most complex model had best model performance. Compared to the reference model, including both model extensions reduced the predicted drought frequency experienced by peatmoss with around 50%. Moreover, projected climate change is expected to reduce predicted peatmoss drought stress with about 20% for the studied site. In conclusion, this study shows that including rainwater storage in the peatmoss layer and/or peat volume

  11. Assessing the impacts of precipitation bias on distributed hydrologic model calibration and prediction accuracy

    NASA Astrophysics Data System (ADS)

    Looper, Jonathan P.; Vieux, Baxter E.; Moreno, Maria A.

    2012-02-01

    SummaryPhysics-based distributed (PBD) hydrologic models predict runoff throughout a basin using the laws of conservation of mass and momentum, and benefit from more accurate and representative precipitation input. V flo™ is a gridded distributed hydrologic model that predicts runoff and continuously updates soil moisture. As a participating model in the second Distributed Model Intercomparison Project (DMIP2), V flo™ is applied to the Illinois and Blue River basins in Oklahoma. Model parameters are derived from geospatial data for initial setup, and then adjusted to reproduce the observed flow under continuous time-series simulations and on an event basis. Simulation results demonstrate that certain runoff events are governed by saturation excess processes, while in others, infiltration-rate excess processes dominate. Streamflow prediction accuracy is enhanced when multi-sensor precipitation estimates (MPE) are bias corrected through re-analysis of the MPE provided in the DMIP2 experiment, resulting in gauge-corrected precipitation estimates (GCPE). Model calibration identified a set of parameters that minimized objective functions for errors in runoff volume and instantaneous discharge. Simulated streamflow for the Blue and Illinois River basins, have Nash-Sutcliffe efficiency coefficients between 0.61 and 0.68, respectively, for the 1996-2002 period using GCPE. The streamflow prediction accuracy improves by 74% in terms of Nash-Sutcliffe efficiency when GCPE is used during the calibration period. Without model calibration, excellent agreement between hourly simulated and observed discharge is obtained for the Illinois, whereas in the Blue River, adjustment of parameters affecting both saturation and infiltration-rate excess processes were necessary. During the 1996-2002 period, GCPE input was more important than model calibration for the Blue River, while model calibration proved more important for the Illinois River. During the verification period (2002

  12. Application of a global hydrologic prediction system to the Zambezi River Basin (Invited)

    NASA Astrophysics Data System (ADS)

    Voisin, N.; Pappenberger, F.; Buizza, R.; Lettenmaier, D. P.

    2010-12-01

    We evaluate a 10-day globally applicable flood prediction scheme over the Zambezi River basin for the period 2003-2007. The hydrological core of the scheme is the Variable Infiltration Capacity (VIC) hydrology model, which we forced with the European Centre for Medium Range Weather Forecasts (ECMWF) temperature and wind analyses, and the near real-time Tropical Rainfall Monitoring Mission (TRMM) precipitation product (3B42RT) up to the day of forecast. During the forecast period, the VIC model was forced with calibrated and downscaled 10-day forecasts from the ECMWF ensemble prediction system (EPS). We also tested a parallel setup where the EPS ensemble forecasts were interpolated to the 0.25 degree spatial resolution of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions (the EPS was extended to 15 days only in 2006). The 15-day spatially distributed ensemble runoff forecasts were then routed to several locations in the basin. Surrogates for observed daily runoff and streamflow were provided by the reference run, i.e. the VIC simulations forced with ECMWF analysis fields and TRMM precipitation. Mean forecast errors and skills for the two sets of ensemble forecasts are evaluated with respect to the reference on a seasonal basis, and are compared to previous results from a similarly designed study over the Ohio River Basin. The influence on forecast accuracy of basin drainage area, hydroclimatic diversity within the basin, and storm type on forecast skill scores is evaluated.

  13. Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

    SciTech Connect

    Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

    2012-08-15

    We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

  14. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

    NASA Astrophysics Data System (ADS)

    Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

    2015-04-01

    This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

  15. Predictable nonlinear dynamics: Advances and limitations

    SciTech Connect

    Anosov, L.A.; Butkovskii, O.Y.; Kravtsov, Y.A.; Surovyatkina, E.D.

    1996-06-01

    Methods for reconstruction chaotic dynamical system structure directly from experimental time series are described. Effectiveness of general methods is illustrated with the results of numerical simulation. It is of common interest that from the single time series it is possible to reconstruct a set of interconnected variables. Predictive power of dynamical models, provided by the nonlinear dynamics inverse problem solution, is limited firstly by the noise level in the system under study and is characterized by the horizon of predictability. New physical results are presented, concerning nonstationary and bifurcation nonlinear systems: (1) algorithms for revealing of nonstationarity in random-like chaotic time-series are suggested based on discriminant analysis with nonlinear discriminant function; (2) an opportunity is established to predict the final state in bifurcation system with quickly varying control parameters; (3) hysteresis is founded out in bifurcation system with quickly varying parameters; (4) delayed correlation {l_angle}noise-prediction error{r_angle} in chaotic systems is revealed. {copyright} {ital 1996 American Institute of Physics.}

  16. A decade of advancement in understanding of rangeland hydrology and erosion and the effects of conservation practices

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Over the past decade the USDA-ARS Northwest Watershed Research Center (NWRC) has conducted extensive field research to quantify hydrologic and erosion effects of rangeland conservation practices and to develop and advance tools for rangeland assessment and management. Much of what was previously kn...

  17. Operational Rainfall Prediction on Meso-γ Scales for Hydrologic Applications

    NASA Astrophysics Data System (ADS)

    Lee, Tim H.; Georgakakos, Konstantine P.

    1996-04-01

    Presented is a rainfall prediction methodology for application in operational hydrologic forecasting with forecast lead times of 1-6 hours and spatial-resolution scales of 10-30 km. The essential elements of the prediction methodology are a mathematical model for precipitation prediction from surface and upper air meteorological variables; operational forecasts of temperature, pressure, humidity, and wind fields by large-scale numerical weather prediction models; surface and upper air meteorological observations; remote and on-site rainfall observations; and a state estimator for real-time updating from local frequent rainfall observations and for probabilistic predictions. This paper formulates a class of rainfall models suitable for this prediction methodology. The models are based on the differential equation of conservation of cloud and rainwater equivalent mass and on a newly introduced advection equation for a parameter that determines updraft strength. The latter advection equation is a prognostic equation for the strength of convection in space and time. The innovative features of the model formulated and tested are the inclusion of the prognostic equation for the advection of regions of active convection, the formulation of the state estimator component for state updating and probabilistic forecasts, and the utilization of a numerical solution scheme which reduces artificial numerical diffusion and can be used with the state estimator because of its explicit form. Utilization of the prediction model formulated was exemplified in several case studies of summer convection in Oklahoma using data available during routine forecast operations. The case studies show that when verified with radar rainfall data, the model's hourly precipitation predictions over a 20,000 km2 area with a 100-900 km2 resolution are better than simple persistence and explain more than 60% of the observed hourly rainfall variance. Sensitivity studies quantify dependence of rainfall

  18. Tuning stochastic matrix models with hydrologic data to predict the population dynamics of a riverine fish

    USGS Publications Warehouse

    Sakaris, P.C.; Irwin, E.R.

    2010-01-01

    We developed stochastic matrix models to evaluate the effects of hydrologic alteration and variable mortality on the population dynamics of a lotie fish in a regulated river system. Models were applied to a representative lotic fish species, the flathead catfish (Pylodictis olivaris), for which two populations were examined: a native population from a regulated reach of the Coosa River (Alabama, USA) and an introduced population from an unregulated section of the Ocmulgee River (Georgia, USA). Size-classified matrix models were constructed for both populations, and residuals from catch-curve regressions were used as indices of year class strength (i.e., recruitment). A multiple regression model indicated that recruitment of flathead catfish in the Coosa River was positively related to the frequency of spring pulses between 283 and 566 m3/s. For the Ocmulgee River population, multiple regression models indicated that year class strength was negatively related to mean March discharge and positively related to June low flow. When the Coosa population was modeled to experience five consecutive years of favorable hydrologic conditions during a 50-year projection period, it exhibited a substantial spike in size and increased at an overall 0.2% annual rate. When modeled to experience five years of unfavorable hydrologic conditions, the Coosa population initially exhibited a decrease in size but later stabilized and increased at a 0.4% annual rate following the decline. When the Ocmulgee River population was modeled to experience five years of favorable conditions, it exhibited a substantial spike in size and increased at an overall 0.4% annual rate. After the Ocmulgee population experienced five years of unfavorable conditions, a sharp decline in population size was predicted. However, the population quickly recovered, with population size increasing at a 0.3% annual rate following the decline. In general, stochastic population growth in the Ocmulgee River was more

  19. Predicting Production Costs for Advanced Aerospace Vehicles

    NASA Technical Reports Server (NTRS)

    Bao, Han P.; Samareh, J. A.; Weston, R. P.

    2002-01-01

    For early design concepts, the conventional approach to cost is normally some kind of parametric weight-based cost model. There is now ample evidence that this approach can be misleading and inaccurate. By the nature of its development, a parametric cost model requires historical data and is valid only if the new design is analogous to those for which the model was derived. Advanced aerospace vehicles have no historical production data and are nowhere near the vehicles of the past. Using an existing weight-based cost model would only lead to errors and distortions of the true production cost. This paper outlines the development of a process-based cost model in which the physical elements of the vehicle are soared according to a first-order dynamics model. This theoretical cost model, first advocated by early work at MIT, has been expanded to cover the basic structures of an advanced aerospace vehicle. Elemental costs based on the geometry of the design can be summed up to provide an overall estimation of the total production cost for a design configuration. This capability to directly link any design configuration to realistic cost estimation is a key requirement for high payoff MDO problems. Another important consideration in this paper is the handling of part or product complexity. Here the concept of cost modulus is introduced to take into account variability due to different materials, sizes, shapes, precision of fabrication, and equipment requirements. The most important implication of the development of the proposed process-based cost model is that different design configurations can now be quickly related to their cost estimates in a seamless calculation process easily implemented on any spreadsheet tool.

  20. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

    NASA Astrophysics Data System (ADS)

    Erdal, Halil Ibrahim; Karakurt, Onur

    2013-01-01

    SummaryStreamflow forecasting is one of the most important steps in the water resources planning and management. Ensemble techniques such as bagging, boosting and stacking have gained popularity in hydrological forecasting in the recent years. The study investigates the potential usage of two ensemble learning paradigms (i.e., bagging; stochastic gradient boosting) in building classification and regression trees (CARTs) ensembles to advance the streamflow prediction accuracy. The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model. The analytic results indicate that CART outperforms SVR in both training and testing phases. Although the obtained results of CART model in training phase are considerable, it is not in testing phase. Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners. Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models. Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting.

  1. Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

    NASA Astrophysics Data System (ADS)

    Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

    2016-04-01

    The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood

  2. Hydrologic Forecasting at the US National Weather Service in the 21st Century: Transition from the NWS River Forecast System (NWSRFS) to the Community Hydrologic Prediction System (CHPS)

    NASA Astrophysics Data System (ADS)

    Restrepo, Pedro; Roe, Jon; Dietz, Christine; Werner, Micha; Gijsbers, Peter; Hartman, Robert; Opitz, Harold; Olsen, Billy; Halquist, John; Shedd, Robert

    2010-05-01

    The US National Weather Service developed the River Forecast System (NWSRFS) since the 1970s as the platform for performing hydrologic forecasts. The system, originally developed for the computers of that era, was optimized for speed of execution and compact and fast data storage and retrieval. However, with modern computers those features became less of a driver, and, instead, the ability to maintain and transition of new developments in data and modeling research into operations have become the top system priorities for hydrologic forecasting software applications. To address those two new priorities, and to allow the hydrologic research community at large to be able to contribute models and forecasting techniques, the National Weather Service proposed the development of the Community Hydrologic Prediction System (CHPS). CHPS must be sufficiently flexible not only to ensure current operational models and data remain available, but also to integrate readily modeling approaches and data from the wider community of practitioners and scientists involved in hydro-meteorological forecasting. Portability considerations require the computational infrastructure to be programmed in a language such as Java, and data formats conform to open standards such as XML. After examining a number of potential candidates, the NWS settled on the Delft Flood Early Warning System (Delft FEWS) from Deltares as the basis for CHPS, since it shares the basic design characteristics, the underlying community philosophy and was being successfully used in operations in several countries. This paper describes the characteristics of CHPS and the transition path to make it operational and available to the community.

  3. Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

    NASA Astrophysics Data System (ADS)

    Fang, X.; Pomeroy, J. W.; Ellis, C. R.; MacDonald, M. K.; DeBeer, C. M.; Brown, T.

    2013-04-01

    One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (~ 9.4 km2), located in the Front Ranges of the Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil, and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during the period 2005-2011 and were compared with detailed field observations of several hydrological cycle components. The results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during the period 2007-2011, with a small bias and normalised root mean square difference (NRMSD) ranging from 40 to 42% for the subalpine conifer forests and from 31 to 67% for the alpine tundra and treeline larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29%. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during the period 2006-2011, with a NRMSD ranging from 17 to 39%, but with consistent overestimation of 7 to 34%. Evaluations of seasonal streamflow during the period 2006-2011 revealed that the model generally predicted well compared to observations at the basin scale, with a NRMSD of 60% and small model bias (1%), while at the sub-basin scale NRMSDs were larger, ranging from 72 to 76%, though overestimation or underestimation for the cumulative seasonal discharge was within 29%. Timing of discharge was better predicted at the Marmot Creek basin outlet, having a Nash

  4. Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains

    NASA Astrophysics Data System (ADS)

    Fang, X.; Pomeroy, J. W.; Ellis, C. R.; MacDonald, M. K.; DeBeer, C. M.; Brown, T.

    2012-11-01

    One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (~ 9.4km2), located in the Front Ranges of Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during 2005-2011 and were compared with detailed field observations of several hydrological cycle components. Results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during 2007-2011, with a small bias and normalized root mean square difference (NRMSD) ranging from 40 to 42% for the subalpine conifer forests and from 31 to 67% for the alpine tundra and tree-line larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29%. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during 2006-2011, with a NRMSD ranging from 17% to 39%, but with consistent overestimation of 7 to 34%. Evaluations of seasonal streamflow during 2006-2011 revealed the model generally predicted well compared to observations at the basin scale, with a NRMSD of 77% and small model bias (6%), but at the sub-basin scale NRMSD were larger, ranging from 86 to 106%; though overestimation or underestimation for the cumulative seasonal discharge was within 24%. Timing of discharge was better predicted at the Marmot Creek basin outlet having a Nash-Sutcliffe efficiency (NSE) of 0.31 compared to the outlets of the sub

  5. Numerical daemons in hydrological modeling: Effects on uncertainty assessment, sensitivity analysis and model predictions

    NASA Astrophysics Data System (ADS)

    Kavetski, D.; Clark, M. P.; Fenicia, F.

    2011-12-01

    Hydrologists often face sources of uncertainty that dwarf those normally encountered in many engineering and scientific disciplines. Especially when representing large scale integrated systems, internal heterogeneities such as stream networks, preferential flowpaths, vegetation, etc, are necessarily represented with a considerable degree of lumping. The inputs to these models are themselves often the products of sparse observational networks. Given the simplifications inherent in environmental models, especially lumped conceptual models, does it really matter how they are implemented? At the same time, given the complexities usually found in the response surfaces of hydrological models, increasingly sophisticated analysis methodologies are being proposed for sensitivity analysis, parameter calibration and uncertainty assessment. Quite remarkably, rather than being caused by the model structure/equations themselves, in many cases model analysis complexities are consequences of seemingly trivial aspects of the model implementation - often, literally, whether the start-of-step or end-of-step fluxes are used! The extent of problems can be staggering, including (i) degraded performance of parameter optimization and uncertainty analysis algorithms, (ii) erroneous and/or misleading conclusions of sensitivity analysis, parameter inference and model interpretations and, finally, (iii) poor reliability of a calibrated model in predictive applications. While the often nontrivial behavior of numerical approximations has long been recognized in applied mathematics and in physically-oriented fields of environmental sciences, it remains a problematic issue in many environmental modeling applications. Perhaps detailed attention to numerics is only warranted for complicated engineering models? Would not numerical errors be an insignificant component of total uncertainty when typical data and model approximations are present? Is this really a serious issue beyond some rare isolated

  6. Predicting Malignancy in Thyroid Nodules: Molecular Advances

    PubMed Central

    Melck, Adrienne L.; Yip, Linwah

    2016-01-01

    Over the last several years, a clearer understanding of the genetic alterations underlying thyroid carcinogenesis has developed. This knowledge can be utilized to tackle one of the greatest challenges facing thyroidologists: management of the indeterminate thyroid nodule. Despite the accuracy of fine needle aspiration cytology, many patients undergo invasive surgery in order to determine if a follicular or Hurthle cell neoplasm is malignant, and better diagnostic tools are required. A number of biomarkers have recently been studied and show promise in this setting. In particular, BRAF, RAS, PAX8-PPARγ, microRNAs and loss of heterozygosity have each been demonstrated as useful molecular tools for predicting malignancy and can thereby guide decisions regarding surgical management of nodular thyroid disease. This review summarizes the current literature surrounding each of these markers and highlights our institution’s prospective analysis of these markers and their subsequent incorporation into our management algorithms for thyroid nodules. PMID:21818817

  7. Radar monitoring of hydrology in Maryland's forested coastal plain wetlands: Implications for predicted climate change and improved mapping

    NASA Astrophysics Data System (ADS)

    Weiner Lang, Megan

    Wetlands provide important services to society but Mid-Atlantic wetlands are at high risk for loss, with forested wetlands being especially vulnerable. Hydrology (flooding and soil moisture) controls wetland function and extent but it may be altered due to changes in climate and anthropogenic influence. Wetland hydrology must better understood in order to predict and mitigate the impact of these changes. Broad-scale forested wetland hydrology is difficult to monitor using ground-based and traditional remote sensing methods. C-band synthetic aperture radar (SAR) data could improve the capability to monitor forested wetland hydrology but the abilities and limitations of these data need further investigation. This study examined: (1) the link between climate and wetland hydrology; (2) the ability of ENVISAT SAR (C-HH and C-VV) data to monitor inundation and soil moisture in forested wetlands; (3) limitations inherent to C-band data (incidence angle, polarization, and phenology) when monitoring forested wetland hydrology; and (4) the accuracy of forested wetland maps produced using SAR data. The study was primarily conducted near the Patuxent River in Maryland but the influence of incidence angle was considered along the Roanoke River in North Carolina. This study showed: (1) climate was highly correlated with wetland inundation; (2) significant differences in C-VV and C-HH backscatter existed between forested areas of varying hydrology (uplands and wetlands) throughout the year; (3) C-HH backscatter was better correlated to hydrology than C-VV backscatter; (4) correlations were stronger during the leaf-off season; (5) the difference in backscatter between flooded and non-flooded areas did not sharply decline with incidence angle, as predicted; and (6) maps produced using SAR data had relatively high accuracy levels. Based on these findings, I concluded that hydrology is influenced by climate at the study site, and C-HH data should be able to monitor changes in

  8. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation

    NASA Astrophysics Data System (ADS)

    Renard, Benjamin; Kavetski, Dmitri; Leblois, Etienne; Thyer, Mark; Kuczera, George; Franks, Stewart W.

    2011-11-01

    This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data and incorporating them into the Bayesian total error analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense rain gauge data are available to characterize the uncertainty in the catchment average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.

  9. NWS-CHPS, the Community Hydrologic Prediction System is operational (Invited)

    NASA Astrophysics Data System (ADS)

    Gijsbers, P.; Brunner, C.; Cajina, L.; Roe, J.; Welles, E.

    2010-12-01

    Late summer 2010, the NOAA/NWS Northwest and the Arkansas-Red Basin Forecasting Centers (RFCs) moved their primary forecasting operations to CHPS. CHPS, the Community Hydrologic Prediction System, is the new operational platform for real-time hydrological forecasting, replacing the former NWS River Forecasting System. CHPS is based on the DelftFEWS framework for operational forecasting systems, developed by Deltares, The Netherlands. With the choice for DelftFEWS, the US-NWS joined the international FEWS user community which has a large user base in Europe and is upcoming in South East Asia and Australia. Those users have chosen DelftFEWS as their operational platfrom since its xml/NetCDF based model interface concept allows them to plug-in their own models, covering a wide variety of domain models with different granularities using model adapters. Currently CHPS uses a sequence of single hydrological process models lumped at basin scale, extracted from NWSRFS, sometimes combined with HEC models (RAS and ResSim). Many other agencies use more integrated, sometimes fully distributed, hydrological models such as HBV, TOPKAPI, TOPMODEL, or HEC-HMS often in combination with commercial hydrodynamic models such as Mike11, ISIS or SOBEK. DelftFEWS also has a track record in operational groundwater, water quality, harmful algae bloom and coastal storm surge forecasting applications. An OpenMI2 adapter will be implemented over the next year to enhance integrated model capabilities by combining different sets of models in an OpenMI model composition. With these capabilities, CHPS can become an attractive community platform where academics work with federal agencies such as the NWS in implementing and transferring new knowledge and models into an operational framework. Operational forecasting demands ensemble based capabilities from CHPS to increase confidence in short, medium and long range forecasts. To address this need, NWS is developing HEFS, the Hydrologic Ensemble

  10. Analysis of Streamflow Predictive Uncertainty using Multiple Hydrologic Models in Climate Change Impact Study

    NASA Astrophysics Data System (ADS)

    Yang, P.; Najafi, M.; Moradkhani, H.

    2010-12-01

    Based on the statistically downscaled outputs from 8 global climate model projections and 2 emission scenarios we assess the uncertainties associated with GCMs and hydrologic models by means of multi-modeling. As there is no conceivable reason that any hydrologic model is performing better under all circumstances, four hydrologic models are selected for the hydrologic impact study: the Sacramento Soil Moisture Accounting (SAC-SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite-Mather model (TM) and the Precipitation Runoff Modeling System (PRMS). Three objective functions are adopted to calibrate each model. The hydrologic model simulations are combined using the Bayesian Model Averaging (BMA) method. This study shows that the application of the BMA in analyzing the models ensemble is useful in minimizing the uncertainty in selecting the hydrologic model selection. It is also concluded that the hydrologic model uncertainty is considerably smaller than GCM uncertainty, except during the dry season.

  11. A Primer In Advanced Fatigue Life Prediction Methods

    NASA Technical Reports Server (NTRS)

    Halford, Gary R.

    2000-01-01

    Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

  12. Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

    NASA Astrophysics Data System (ADS)

    Kuo, Chun-Chao; Gan, Thian Yew; Yu, Pao-Shan

    2010-06-01

    SummaryA combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.

  13. Evaluation of Hydrologic Models to Predict Sediment Export With Changing Land Use in Leeward Hawaiian Watersheds

    NASA Astrophysics Data System (ADS)

    Falinski, K. A.; Oleson, K.; Nielson, J.

    2014-12-01

    Land-based sediments are a key threat to shallow coral reef ecosystems in Hawaii. Estimating sediment export is a critical step to being able to connect future land use changes with changes in sediment released to the coastal zone. However, empirically- and process-based hydrological models have proven difficult to adapt to Hawaii's geography, adding significant uncertainty to using available decision support tools. Four soil loss and sediment yield models, InVEST, N-SPECT, SWAT and GSSHA, were compared. Data including precipitation, flow discharge, and suspended sediment concentration were compiled from four leeward watersheds in the Hawaiian Islands. These were combined with the most recently available GIS data on soils, rainfall, land use and 10-m elevation. Results show that annual sediment export is typically underpredicted by an order of magnitude in the models. Moreover, soil loss predictions are spatially incongruent with field observations. Model results overestimate soil loss in the steep forested zones, where field observations show source material to be limited, and are not able to adequately capture human- and animal-disturbed material that connects hydrologically with the stream network. We suggest that the differences stem from a mismatch of processes that source sediments, including stream channel erosion and storage and shallow landslides, which are not included in all the models that are typically used for decision support. Moreover, different modeling platforms use different transport equations, which have not been validated for steep, mountainous watersheds. Changes in land use, such as new developments or cover crops, are obscured by models which consider steeply-sloped areas to be the primary source of sediment. The comparison suggests that decision support tools for Hawaii need a different approach for predicting sediment export with changing land use.

  14. Coupling crop growth and hydrologic models to predict crop yield with spatial analysis technologies

    NASA Astrophysics Data System (ADS)

    Jia, Yangwen; Shen, Suhui; Niu, Cunwen; Qiu, Yaqin; Wang, Hao; Liu, Yu

    2011-01-01

    This paper analyzes climate change impact on crop yield of winter wheat, a main crop in the water-stressed Haihe River Basin in North China. An integrated analysis was carried out by coupling the World Food Studies (WOFOST) crop growth model and the distributed hydrological model describing the water and energy transfer processes in large river basins (WEP-L). Various spatial analysis technologies, including remote sensing and geographical information system, were woven together to support model calibration and validation. The WOFOST model was calibrated and validated using the winter wheat data collected in two successive years. Effort was then extended to calibrate and validate the WEP-L distributed hydrologic model for the whole basin. Such an effort was collectively supported by using the remote sensing evapotranspiration and biomass data, the in situ river flow data, and the wheat yield statistical data. With this integration, the wheat yield from 2010 to 2030 can be predicted under the given climate change impact corresponding to Intergovernmental Panel on Climate Change A1B, A2, and B1 scenarios. Given the prescribed climate change scenarios, at the basin-scale, the winter wheat yield may increase in terms of the annual average; however, the long-term trend is geared toward a decreasing yield with significant fluctuations. The colder hilly areas with current lower yield may significantly increase due to possible future temperature rise while the warmer plain areas with current higher yield may slightly increase or decrease. Despite the data collected thus far, it is evident that further studies are needed to reduce the uncertainties of these predictions of climate change effect on winter wheat grain yield.

  15. Hydrologic monitoring in 1-km2 headwater catchments in Sierra Nevada forests for predictive modeling of hydrologic response to forest treatments across 140-km2 firesheds

    NASA Astrophysics Data System (ADS)

    Saksa, P. C.; Bales, R. C.; Conklin, M. H.; Martin, S. E.; Rice, R.

    2010-12-01

    As part of the Sierra Nevada Adaptive Management Project, an eight-year study designed to measure the impacts of forest treatments (thinning, mastication, controlled burns) on multiple forest attributes, four headwater catchments were established to provide data on hydrologic response to treatments. These 1-km2 study catchments are each sited within 40-100 km2 firesheds, which in this case largely follow watershed boundaries, and which are the larger study areas for informing adaptive management of approximately 3,000 km2 of mixed-conifer forest in California’s central and southern Sierra Nevada. The aim of the hydrologic design was to put in place a ground-based monitoring network that would measure hydrologic attributes at representative locations, and when combined with remotely sensed data, provide a basis for predictive modeling of the larger study area. The selected locations employ instrument clusters, or groupings of instruments in a compact arrangement, to maximize the number of measurements possible and accessibility to the monitoring sites. The two study firesheds , located in the Tahoe and Sierra National Forests, cover a total of about 140-km2. Within each fireshed, two meteorological stations were placed near 1650-m and 2150-m, spanning the precipitation gradient from lower-elevation rain-dominated to higher-elevation snow-dominated systems. Two headwater streams draining approximately 1-km2 are monitored for stage, discharge, electrical conductivity, and sediment movement. Additionally, instrument nodes to monitor temperature, snow depth and soil moisture are installed within 0.5-1 km of the outlet and meterological stations. These nodes were placed to monitor end members of aspect, slope, elevation and canopy cover, which set the boundaries for the model outputs. High-resolution LiDAR provides the topographic and distributed vegetation characteristics, which are combined with field surveys and standard soils information to define the modeling

  16. Evaluating the impacts of soil data on hydrological and nonpoint source pollution prediction.

    PubMed

    Chen, Lei; Wang, Guobo; Zhong, Yucen; Shen, Zhenyao

    2016-09-01

    Soil data are one key input for most hydrological and nonpoint source (H/NPS) models, and quantifying the error transmission from soil data to H/NPS predictions is of great importance. In this study, two typical soil datasets were compared using the Soil and Water Assessment Tool (SWAT) in a typical mountainous watershed, the Three Gorges Reservoir Region, China. Besides, the effects of soil data resolution were evaluated, and the error transmission from soil data to watershed management strategy was assessed. The results indicate that model outputs are not sensitive to changes of soil data resolution but the choice of soil data greatly impacts the application of watershed models, in terms of the goodness-of-fit indicator, predicted data and related uncertainty. This soil data-induced error would be inevitably magnified from the flow simulation to the NPS prediction stage. This study could indicate that the choice of soil data will lead to significant differences in management schemes for specific pollution periods. These results provide information on the impacts of soil data on the functionality of watershed models and valuable information for the appropriateness of each soil database. PMID:27135563

  17. Upper Limits of Predictability in Long-Range Climate/Hydrologic Forecasts

    NASA Technical Reports Server (NTRS)

    Koster, R. D.; Suarez, M. J.; Heiser, M.

    1998-01-01

    The accurate forecasting of el nino or la nina conditions in the tropical Pacific can potentially lead to valuable predictions of hydrological anomalies over land at seasonal to interannual timescales. Even with highly accurate earth system models, though, our ability to generate these continental forecasts will always be limited by the chaotic nature of the atmospheric circulation. The nature of this fundamental limitation is explored through the use of 16-member ensembles of multi-decade GCM simulations. In each simulation of the first ensemble, sea surface temperatures (SSTs) are given the same realistic interannual variations over a 45-year period, and land surface state is allowed to evolve with that of the atmosphere. Analysis of the results shows that the SSTs control the temporal organization of continental precipitation anomalies to a significant extent in the tropics and to a much smaller extent in midlatitudes. In each simulation of the second ensemble, we prescribe SSTs as before, but we also prescribe interannual variations in the low frequency component of evaporation efficiency over land. Thus, in the second ensemble, we effectively make the extreme assumption that surface boundary conditions across the globe are perfectly predictable, and we quantify the consistency with which the atmosphere (particularly precipitation) responds to these boundary conditions. The resulting "absolute upper limit" on the predictability of precipitation is found to be quite high in the tropics yet only moderate in many midlatitude regions.

  18. High-speed limnology: using advanced sensors to investigate spatial variability in biogeochemistry and hydrology.

    PubMed

    Crawford, John T; Loken, Luke C; Casson, Nora J; Smith, Colin; Stone, Amanda G; Winslow, Luke A

    2015-01-01

    Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h(-1)) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial-aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data. PMID:25406073

  19. Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

    NASA Astrophysics Data System (ADS)

    Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

    2011-12-01

    In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

  20. Robust quantitative parameter estimation by advanced CMP measurements for vadose zone hydrological studies

    NASA Astrophysics Data System (ADS)

    Koyama, C.; Wang, H.; Khuut, T.; Kawai, T.; Sato, M.

    2015-12-01

    Soil moisture plays a crucial role in the understanding of processes in the vadose zone hydrology. In the last two decades ground penetrating radar (GPR) has been widely discussed has nondestructive measurement technique for soil moisture data. Especially the common mid-point (CMP) technique, which has been used in both seismic and GPR surveys to investigate the vertical velocity profiles, has a very high potential for quantitaive obervsations from the root zone to the ground water aquifer. However, the use is still rather limited today and algorithms for robust quantitative paramter estimation are lacking. In this study we develop an advanced processing scheme for operational soil moisture reetrieval at various depth. Using improved signal processing, together with a semblance - non-normalized cross-correlation sum combined stacking approach and the Dix formula, the interval velocities for multiple soil layers are obtained from the RMS velocities allowing for more accurate estimation of the permittivity at the reflecting point. Where the presence of a water saturated layer, like a groundwater aquifer, can be easily identified by its RMS velocity due to the high contrast compared to the unsaturated zone. By using a new semi-automated measurement technique the acquisition time for a full CMP gather with 1 cm intervals along a 10 m profile can be reduced significantly to under 2 minutes. The method is tested and validated under laboratory conditions in a sand-pit as well as on agricultural fields and beach sand in the Sendai city area. Comparison between CMP estimates and TDR measurements yield a very good agreement with RMSE of 1.5 Vol.-%. The accuracy of depth estimation is validated with errors smaller than 2%. Finally, we demonstrate application of the method in a test site in semi-arid Mongolia, namely the Orkhon River catchment in Bulgan, using commercial 100 MHz and 500 MHz RAMAC GPR antennas. The results demonstrate the suitability of the proposed method for

  1. Recent scientific advances in the use of radar in scientific hydrology

    NASA Technical Reports Server (NTRS)

    Engman, Edwin T.

    1993-01-01

    The data needs in scientific hydrology involve measurements of system states and fluxes. The microwave region is particularly well suited for measuring the system states of soil moisture and snow and the major flux into the earth as rainfall. This paper discusses the unique data needs of hydrology and presents some recent examples from AIRSAR experiments.

  2. Hydrological Aspects of Weather Prediction and Flood Warnings: Report of the Ninth Prospectus Development Team of the U.S. Weather Research Program

    USGS Publications Warehouse

    Droegemeier, K.K.; Smith, J.D.; Businger, S.; Doswell, C., III; Doyle, J.; Duffy, C.; Foufoula-Georgiou, E.; Graziano, T.; James, L.D.; Krajewski, V.; LeMone, M.; Lettenmaier, D.; Mass, C.; Pielke, R., Sr.; Ray, P.; Rutledge, S.; Schaake, J.; Zipser, E.

    2000-01-01

    Among the many natural disasters that disrupt human and industrial activity in the United States each year, including tornadoes, hurricanes, extreme temperatures, and lightning, floods are among the most devastating and rank second in the loss of life. Indeed, the societal impact of floods has increased during the past few years and shows no sign of abating. Although the scientific questions associated with flooding and its accurate prediction are many and complex, an unprecedented opportunity now exists - in light of new observational and computing systems and infrastructures, a much improved understanding of small-scale meteorological and hydrological processes, and the availability of sophisticated numerical models and data assimilation systems - to attack the flood forecasting problem in a comprehensive manner that will yield significant new scientific insights and corresponding practical benefits. The authors present herein a set of recommendations for advancing our understanding of floods via the creation of natural laboratories situated in a variety of local meteorological and hydrological settings. Emphasis is given to floods caused by convection and cold season events, fronts and extratropical cyclones, orographic forcing, and hurricanes and tropical cyclones following landfall. Although the particular research strategies applied within each laboratory setting will necessarily vary, all will share the following principal elements: (a) exploitation of those couplings important to flooding that exist between meteorological and hydrological processes and models; (b) innovative use of operational radars, research radars, satellites, and rain gauges to provide detailed spatial characterizations of precipitation fields and rates, along with the use of this information in hydrological models and for improving and validating microphysical algorithms in meteorological models; (c) comparisons of quantitative precipitation estimation algorithms from both research

  3. Hydrological Aspects of Weather Prediction and Flood Warnings: Report of the Ninth Prospectus Development Team of the U.S. Weather Research Program.

    NASA Astrophysics Data System (ADS)

    Droegemeier, K. K.; Smith, J. D.; Businger, S.; Doswell, C., III; Doyle, J.; Duffy, C.; Foufoula-Georgiou, E.; Graziano, T.; James, L. D.; Krajewski, V.; Lemone, M.; Lettenmaier, D.; Mass, C.; Pielke, R., Sr.; Ray, P.; Rutledge, S.; Schaake, J.; Zipser, E.

    2000-11-01

    Among the many natural disasters that disrupt human and industrial activity in the United States each year, including tornadoes, hurricanes, extreme temperatures, and lightning, floods are among the most devastating and rank second in the loss of life. Indeed, the societal impact of floods has increased during the past few years and shows no sign of abating. Although the scientific questions associated with flooding and its accurate prediction are many and complex, an unprecedented opportunity now exists-in light of new observational and computing systems and infrastructures, a much improved understanding of small-scale meteorological and hydrological processes, and the availability of sophisticated numerical models and data assimilation systems-to attack the flood forecasting problem in a comprehensive manner that will yield significant new scientific insights and corresponding practical benefits. The authors present herein a set of recommendations for advancing our understanding of floods via the creation of natural laboratories situated in a variety of local meteorological and hydrological settings. Emphasis is given to floods caused by convection and cold season events, fronts and extratropical cyclones, orographic forcing, and hurricanes and tropical cyclones following landfall. Although the particular research strategies applied within each laboratory setting will necessarily vary, all will share the following principal elements: (a) exploitation of those couplings important to flooding that exist between meteorological and hydrological processes and models; (b) innovative use of operational radars, research radars, satellites, and rain gauges to provide detailed spatial characterizations of precipitation fields and rates, along with the use of this information in hydrological models and for improving and validating microphysical algorithms in meteorological models; (c) comparisons of quantitative precipitation estimation algorithms from both research

  4. Advanced propeller noise prediction in the time domain

    NASA Technical Reports Server (NTRS)

    Farassat, F.; Dunn, M. H.; Spence, P. L.

    1992-01-01

    The time domain code ASSPIN gives acousticians a powerful technique of advanced propeller noise prediction. Except for nonlinear effects, the code uses exact solutions of the Ffowcs Williams-Hawkings equation with exact blade geometry and kinematics. By including nonaxial inflow, periodic loading noise, and adaptive time steps to accelerate computer execution, the development of this code becomes complete.

  5. Hydrologic similarity, comparative hydrology and hydrologic extremes

    NASA Astrophysics Data System (ADS)

    Wagener, T.; Laaha, G.; Koffler, D.; Singh, R.

    2012-04-01

    Recent years have brought a renewed focus on the issue of hydrologic similarity. What makes two catchments similar and what can we do with this understanding? The reason for this issue being so important lies at least partially in the need for generalization of results in a scientific field, which is limited through the large heterogeneity in our environment. The issue of hydrologic similarity is of course as old as hydrology itself, however, we believe that taking stock is needed from time to time to guide comparative hydrology efforts that have the potential to bring structure into the field of catchment hydrology. Apart from that, catchment similarity is the rational behind any attempt of predicting streamflow at ungauged basins, and a better understanding and definition of hydrologic similarity will enhance our ability to estimate water resources in absence of stream gauges. In this talk we focus on signatures of hydrologic extremes, i.e. flood and low flow characteristics of streamflow. Can similarity concepts relate catchment behavior under both high and low flow extremes? In how far do our understanding and our predictive capability regarding hydrologic extremes benefit from a holistic few of individual catchments, and from a comparative analysis between catchment? We will review different studies and present a meta analysis to highlight the proven and the potential benefit of taking a broader view.

  6. Predicting debris flow occurrence in Eastern Italian Alps based on hydrological and geomorphological modelling

    NASA Astrophysics Data System (ADS)

    Nikolopoulos, Efthymios I.; Borga, Marco; Destro, Elisa; Marchi, Lorenzo

    2015-04-01

    Most of the work so far on the prediction of debris flow occurrence is focused on the identification of critical rainfall conditions. However, findings in the literature have shown that critical rainfall thresholds cannot always accurately identify debris flow occurrence, leading to false detections (positive or negative). One of the main reasons for this limitation is attributed to the fact that critical rainfall thresholds do not account for the characteristics of underlying land surface (e.g. geomorphology, moisture conditions, sediment availability, etc), which are strongly related to debris flow triggering. In addition, in areas where debris flows occur predominantly as a result of channel bed failure (as in many Alpine basins), the triggering factor is runoff, which suggests that identification of critical runoff conditions for debris flow prediction is more pertinent than critical rainfall. The primary objective of this study is to investigate the potential of a triggering index (TI), which combines variables related to runoff generation and channel morphology, for predicting debris flows occurrence. TI is based on a threshold criterion developed on past works (Tognacca et al., 2000; Berti and Simoni, 2005; Gregoretti and Dalla Fontana, 2008) and combines information on unit width peak flow, local channel slope and mean grain size. Estimation of peak discharge is based on the application of a distributed hydrologic model, while local channel slope is derived from a high-resolution (5m) DEM. Scaling functions of peak flows and channel width with drainage area are adopted since it is not possible to measure channel width or simulate peak flow at all channel nodes. TI values are mapped over the channel network thus allowing spatially distributed prediction but instead of identifying debris flow occurrence on single points, we identify their occurrence with reference to the tributary catchment involved. Evaluation of TI is carried out for five different basins

  7. Typology of Hydrologic Prediction Challenges and Implications for Trustworthy Model Development

    NASA Astrophysics Data System (ADS)

    Kumar, P.

    2011-12-01

    for model representation as well as model diagnostics may be developed. The implications of these ideas for developing trustworthy models will be discussed. For some additional details see: Kumar, P. (2011), Typology of hydrologic predictability, Water Resour. Res., 47, W00H05, doi:10.1029/2010WR009769.

  8. Incorporating multi-platform remote sensing products for prediction of post-fire hydrologic recovery

    NASA Astrophysics Data System (ADS)

    Kinoshita, A. M.; Hogue, T. S.; Kim, J.

    2011-12-01

    Wildfires are increasing in intensity and size across the western US, and more than half of the 20 largest fires in California have occurred within the last decade. Development in southern California has increased and as a result many homes at the wildland-urban interface (WUI) are affected by fire events themselves and post-fire processes. Current management efforts are mostly concentrated around immediate post-fire effects (first storm season); however, burned systems are often altered for prolonged periods of time, creating long-term concerns for downstream communities at the WUI. Previous work in two southern Californian watersheds, City Creek and Devil Canyon, shows lack of vegetation recovery and significant changes in annual and seasonal discharge for the post-fire study period (seven years). Applying remotely sensed data streams enhances monitoring of large and ungauged burned areas at high spatial and temporal resolutions. The goal of the current study is to integrate remote sensing data from multiple satellite platforms to improve prediction of the spatial and temporal variability of key hydrological variables controlling post-fire response. Remote sensing data streams from Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat are used to derive a range of land surface parameters and evaluate ecosystem and hydrologic recovery for the Arroyo Seco, an urban-fringe watershed in southern California burned by the 2009 Station Fire. A UCLA remotely-sensed evapotranspiration (ET) product is used to provide insight on vegetation growth and plant water availability. A UCLA MODIS-AMSR-E soil moisture product is used to evaluate the spatial variability of post-fire surface soil moisture and coupled storm runoff response. A range of other parameters, surface temperature, albedo and vegetation indices, are also evaluated to provide insight on the spatial variability of watershed recovery. Predicting the short and long-term risks of post-fire floods, debris

  9. Predictive Biomarkers to Chemoradiation in Locally Advanced Rectal Cancer.

    PubMed

    Conde-Muíño, Raquel; Cuadros, Marta; Zambudio, Natalia; Segura-Jiménez, Inmaculada; Cano, Carlos; Palma, Pablo

    2015-01-01

    There has been a high local recurrence rate in rectal cancer. Besides improvements in surgical techniques, both neoadjuvant short-course radiotherapy and long-course chemoradiation improve oncological results. Approximately 40-60% of rectal cancer patients treated with neoadjuvant chemoradiation achieve some degree of pathologic response. However, there is no effective method of predicting which patients will respond to neoadjuvant treatment. Recent studies have evaluated the potential of genetic biomarkers to predict outcome in locally advanced rectal adenocarcinoma treated with neoadjuvant chemoradiation. The articles produced by the PubMed search were reviewed for those specifically addressing a genetic profile's ability to predict response to neoadjuvant treatment in rectal cancer. Although tissue gene microarray profiling has led to promising data in cancer, to date, none of the identified signatures or molecular markers in locally advanced rectal cancer has been successfully validated as a diagnostic or prognostic tool applicable to routine clinical practice. PMID:26504848

  10. Predictive Biomarkers to Chemoradiation in Locally Advanced Rectal Cancer

    PubMed Central

    Conde-Muíño, Raquel; Cuadros, Marta; Zambudio, Natalia; Segura-Jiménez, Inmaculada; Cano, Carlos; Palma, Pablo

    2015-01-01

    There has been a high local recurrence rate in rectal cancer. Besides improvements in surgical techniques, both neoadjuvant short-course radiotherapy and long-course chemoradiation improve oncological results. Approximately 40–60% of rectal cancer patients treated with neoadjuvant chemoradiation achieve some degree of pathologic response. However, there is no effective method of predicting which patients will respond to neoadjuvant treatment. Recent studies have evaluated the potential of genetic biomarkers to predict outcome in locally advanced rectal adenocarcinoma treated with neoadjuvant chemoradiation. The articles produced by the PubMed search were reviewed for those specifically addressing a genetic profile's ability to predict response to neoadjuvant treatment in rectal cancer. Although tissue gene microarray profiling has led to promising data in cancer, to date, none of the identified signatures or molecular markers in locally advanced rectal cancer has been successfully validated as a diagnostic or prognostic tool applicable to routine clinical practice. PMID:26504848

  11. Comparison of three methods for the optimal allocation of hydrological model participation in an Ensemble Prediction System

    NASA Astrophysics Data System (ADS)

    Brochero, D.; Anctil, F.; Gagné, C.

    2012-04-01

    Today, the availability of the Meteorological Ensemble Prediction Systems (MEPS) and its subsequent coupling with multiple hydrological models offer the possibility of building Hydrological Ensemble Prediction Systems (HEPS) consisting of a large number of members. However, this task is complex both in terms of the coupling of information and of the computational time, which may create an operational barrier. The evaluation of the prominence of each hydrological members can be seen as a non-parametric post-processing stage that seeks finding the optimal participation of the hydrological models (in a fashion similar to the Bayesian model averaging technique), maintaining or improving the quality of a probabilistic forecasts based on only x members drawn from a super ensemble of d members, thus allowing the reduction of the task required to issue the probabilistic forecast. The main objective of the current work consists in assessing the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF), i.e. an 800-member HEPS. In a previous work (Brochero et al., 2011a, b), we demonstrated that the proportion of members allocated to each hydrological model is a sufficient criterion to reduce the number of hydrological members while improving the balance of the scores, taking into account interchangeability of the ECMWF MEPS. Here, we compare the proportion of members allocated to each hydrological model derived from three non-parametric techniques: correlation analysis of hydrological members, Backward Greedy Selection (BGS) and Nondominated Sorting Genetic Algorithm (NSGA II). The last two techniques allude to techniques developed in machine learning, in a multicriteria framework exploiting the relationship between bias, reliability, and the number of members of the

  12. Hydrologic Connections and Landscape Metrics to Advance Ecosystem Goods and Services in Tampa Bay Watershed

    EPA Science Inventory

    Understanding the hydrological characteristics of coastal wetlands across land use gradients ranging from natural to urban to agricultural is important for significantly enhancing our ability to utilize environmental data in interpreting ecosystem condition and processes. Here we...

  13. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model

    NASA Astrophysics Data System (ADS)

    Jasper, Karsten; Gurtz, Joachim; Lang, Herbert

    2002-10-01

    Flood forecasting may be improved by coupling atmospheric and hydrological models. To investigate the current potential of such an approach in complex mountain watersheds, the authors carried out a number of combined high-resolution one-way driven model experiments to generate runoff hydrographs for seven extreme flood events which occurred in the Lago Maggiore basin between 1993 and 2000. The Alpine Ticino-Verzasca-Maggia basin (2627 km 2) is located directly to the south of the main Alpine ridge embracing a great part of the drainage area of Lago Maggiore. For this basin, the grid-based hydrological catchment model WaSiM-ETH was employed to determine the continuous runoff hydrographs. In the model experiments, two different sets of meteorological input data were used: (1) surface observation data from station measurements and from weather radar, and (2) forecast data from five different high-resolution numerical weather prediction (NWP) models with grid cell sizes between 2 and 14 km. This paper presents and compares selected results of these flood runoff simulations with particular attention to the experimental design of the model coupling. The configuration and initialization of the hydrological model runs are outlined as well as the down-scale techniques which proved to provide an adequate spatial interpolation of the meteorological variables onto the 500 m×500 m grid of the hydrological model. In order to evaluate the various hydrological model results as generated from the different outputs from the five NWP models, some coupled experiments with 'non-standard' NWP model outputs have been carried out. In particular, the results of these sensitivity studies point to inherent limits of high-resolution flood runoff predictions in complex mountain terrain.

  14. A study on WRF radar data assimilation for hydrological rainfall prediction

    NASA Astrophysics Data System (ADS)

    Liu, J.; Bray, M.; Han, D.

    2013-08-01

    Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are

  15. Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan

    NASA Astrophysics Data System (ADS)

    Bocchiola, D.; Diolaiuti, G.; Soncini, A.; Mihalcea, C.; D'Agata, C.; Mayer, C.; Lambrecht, A.; Rosso, R.; Smiraglia, C.

    2011-04-01

    In the mountain regions of the Hindu Kush, Karakoram and Himalaya (HKH) the "third polar ice cap" of our planet, glaciers play the role of "water towers" by providing significant amount of melt water, especially in the dry season, essential for agriculture, drinking purposes, and hydropower production. Recently, most glaciers in the HKH have been retreating and losing mass, mainly due to significant regional warming, thus calling for assessment of future water resources availability for populations down slope. However, hydrology of these high altitude catchments is poorly studied and little understood. Most such catchments are poorly gauged, thus posing major issues in flow prediction therein, and representing in facts typical grounds of application of PUB concepts, where simple and portable hydrological modeling based upon scarce data amount is necessary for water budget estimation, and prediction under climate change conditions. In this preliminarily study, future (2060) hydrological flows in a particular watershed (Shigar river at Shigar, ca. 7000 km2), nested within the upper Indus basin and fed by seasonal melt from major glaciers, are investigated. The study is carried out under the umbrella of the SHARE-Paprika project, aiming at evaluating the impact of climate change upon hydrology of the upper Indus river. We set up a minimal hydrological model, tuned against a short series of observed ground climatic data from a number of stations in the area, in situ measured ice ablation data, and remotely sensed snow cover data. The future, locally adjusted, precipitation and temperature fields for the reference decade 2050-2059 from CCSM3 model, available within the IPCC's panel, are then fed to the hydrological model. We adopt four different glaciers' cover scenarios, to test sensitivity to decreased glacierized areas. The projected flow duration curves, and some selected flow descriptors are evaluated. The uncertainty of the results is then addressed, and use of

  16. Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan

    NASA Astrophysics Data System (ADS)

    Bocchiola, D.; Diolaiuti, G.; Soncini, A.; Mihalcea, C.; D'Agata, C.; Mayer, C.; Lambrecht, A.; Rosso, R.; Smiraglia, C.

    2011-07-01

    In the mountain regions of the Hindu Kush, Karakoram and Himalaya (HKH) the "third polar ice cap" of our planet, glaciers play the role of "water towers" by providing significant amount of melt water, especially in the dry season, essential for agriculture, drinking purposes, and hydropower production. Recently, most glaciers in the HKH have been retreating and losing mass, mainly due to significant regional warming, thus calling for assessment of future water resources availability for populations down slope. However, hydrology of these high altitude catchments is poorly studied and little understood. Most such catchments are poorly gauged, thus posing major issues in flow prediction therein, and representing in fact typical grounds of application of PUB concepts, where simple and portable hydrological modeling based upon scarce data amount is necessary for water budget estimation, and prediction under climate change conditions. In this preliminarily study, future (2060) hydrological flows in a particular watershed (Shigar river at Shigar, ca. 7000 km2), nested within the upper Indus basin and fed by seasonal melt from major glaciers, are investigated. The study is carried out under the umbrella of the SHARE-Paprika project, aiming at evaluating the impact of climate change upon hydrology of the upper Indus river. We set up a minimal hydrological model, tuned against a short series of observed ground climatic data from a number of stations in the area, in situ measured ice ablation data, and remotely sensed snow cover data. The future, locally adjusted, precipitation and temperature fields for the reference decade 2050-2059 from CCSM3 model, available within the IPCC's panel, are then fed to the hydrological model. We adopt four different glaciers' cover scenarios, to test sensitivity to decreased glacierized areas. The projected flow duration curves, and some selected flow descriptors are evaluated. The uncertainty of the results is then addressed, and use of the

  17. Integrating understanding of hydrology, geomorphology and ecology to better predict periphyton abundance in New Zealand rivers

    NASA Astrophysics Data System (ADS)

    Hoyle, Jo; Kilroy, Cathy; Hicks, Murray

    2015-04-01

    Periphyton (the algae dominated community that grows on the bed of rivers) provide a rich food source for the upper trophic levels of stream ecosystems and can also provide an important ecological service by removing dissolved nutrients and contaminants from the flow. However, in excess, periphyton can have negative effects on habitat quality, water chemistry and biodiversity, and can reduce recreation and aesthetic values. The abundance of periphyton in rivers is influenced by a number of factors, but the two key factors that can be directly influenced by human activities are flow regime and nutrient concentrations. River managers in New Zealand are required to set objectives for periphyton abundance that meet or exceed national bottom lines, and they then need to set limits on freshwater quality and quantity in their region to ensure these objectives are met. Consequently, the ability to predict periphyton abundance under different conditions is crucial for management of rivers to protect ecological and other values. Establishing quantitative relationships between periphyton abundance, hydrologic regimes and nutrient concentrations has proven to be difficult but remains an urgent priority in New Zealand. A common index for predicting periphyton abundance has been the frequency of floods greater than 3 times the median flow (FRE3), and this has been successful on a regional average but can be quite unreliable on a site-specific basis. This stems largely from our limited ability to transform flow data into ecologically meaningful physical processes that directly affect periphyton removal (e.g., drag, abrasion, bed movement). The research we will present examines whether geomorphic variables, such as frequency of bed movement, are useful co-predictors in periphyton abundance-flow-nutrient relationships. We collected data on channel topography and bed material size for 20 reaches in the Manawatu-Wanganui Region which have at least 5 years of flow, nutrient

  18. Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

    NASA Technical Reports Server (NTRS)

    Miller, L. D.; Tom, C.; Nualchawee, K.

    1977-01-01

    A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

  19. Sustainable co-evolution of society, ecology and hydrology: forward-looking modelling and prediction of the ecosystem and hydrology of the Lake of Monate - Italy

    NASA Astrophysics Data System (ADS)

    Montanari, Alberto; Attilio, Castellarin; Cervi, Federico

    2016-04-01

    The catchment of the Lake of Monate, in Northern Italy, is a unique example of sustainable and long-term co-evolution of society, exploitation of environmental resources, ecology and hydrology. The catchment is intensively managed since Roman times for the extraction of limestone and the whole basin area was intensively urbanized in recent times, so that the lake is now placed within a profoundly impacted environment. Notwithstanding the above relevant anthropogenic activity, the ecosystem of the lake is still very close to pristine conditions, therefore offering unique research opportunities. Sustainable co-evolution was ensured by the absence of significant surface inflows to the lake, which is mainly alimented by groundwater flows, and a wise and forward looking land use planning and management since ancient times. Today, the increasing pace of limestone extraction, and consequent land recovery, as well as urbanization, poses the need for an improved understanding of sustainability, to support long term prediction and planning. The target is to ensure that the ecosystemic value of the lake is preserved for the benefit of future generations and societal development. The above need calls for improved modelling tools where the co-evolution of society, ecology and hydrology is modelled by focusing on the time scales of the related interactions and the planning horizon. A theoretical framework will be presented to identify the above relevant scales and to properly incorporate the feedbacks between human activity and natural systems.

  20. PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES

    EPA Science Inventory

    Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

  1. Advancing Satellite-Based Flood Prediction in Complex Terrain Using High-Resolution Numerical Weather Prediction

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Anagnostou, E. N.; Nikolopoulos, E. I.; Bartsotas, N. S.

    2015-12-01

    Floods constitute one of the most significant and frequent natural hazard in mountainous regions. Satellite-based precipitation products offer in many cases the only available source of QPE. However, satellite-based QPE over complex terrain suffer from significant bias that limits their applicability for hydrologic modeling. In this work we investigate the potential of a new correction procedure, which involves the use of high-resolution numerical weather prediction (NWP) model simulations to adjust satellite QPE. Adjustment is based on the pdf matching of satellite and NWP (used as reference) precipitation distribution. The impact of correction procedure on simulating the hydrologic response is examined for 15 storm events that generated floods over the mountainous Upper Adige region of Northern Italy. Atmospheric simulations were performed at 1-km resolution from a state-of-the-art atmospheric model (RAMS/ICLAMS). The proposed error correction procedure was then applied on the widely used TRMM 3B42 satellite precipitation product and the evaluation of the correction was based on independent in situ precipitation measurements from a dense rain gauge network (1 gauge / 70 km2) available in the study area. Satellite QPE, before and after correction, are used to simulate flood response using ARFFS (Adige River Flood Forecasting System), a semi-distributed hydrologic model, which is used for operational flood forecasting in the region. Results showed that bias in satellite QPE before correction was significant and had a tremendous impact on the simulation of flood peak, however the correction procedure was able to reduce bias in QPE and therefore improve considerably the simulated flood hydrograph.

  2. Inter-annual and inter-catchment variability of hydrologic partitioning: The importance of the Horton index to improve hydrologic predictions in a changing environment (Invited)

    NASA Astrophysics Data System (ADS)

    Troch, P. A.; Sivapalan, M.; Ruddell, B. L.; Brooks, P. D.; Durcik, M.; McGrath, G.

    2009-12-01

    In 1933, Horton analyzed the inter-annual variability of growing-season water balance components of the West Branch of the Delaware River at Hancock, NY, and discovered that the ratio of catchment vaporization to catchment wetting (the Horton index) remains almost constant despite large variability in precipitation. Eighty-six years later, Horton’s observation was confirmed using data from 431 MOPEX catchment in the US, but it was noted that the inter-annual variability of the Horton index depends strongly on the annual humidity index. It was also found that catchment vegetation use water more efficiently during drought years (lowest precipitation amount in 30 year record), suggesting that the convergence of biomes toward a common rain-use efficiency leaves a trace in the catchment water balance across climates. Recent work at the Hydrologic Synthesis Summer Institute at UBC, Vancouver, explored different simple top-down modeling approaches to investigate the observations related to the Horton index. All models were capable of predicting the mean Horton index with great accuracy, but were not able to fully explain the inter-annual variability. None of the models explicitly account for vegetation dynamics. It was also explored how knowledge of the Horton index in any given year can predict vegetation response (here measured by MODIS derived maximum NDVI), with surprisingly good results. Therefore, knowledge as to how precipitation is partitioned allows for improved predictions of vegetation response. This leads to an apparent paradox: how can it be that we predict the Horton index accurately with simple models that do not account for vegetation response, while knowledge of the Horton index predicts vegetation response? To resolve this paradox we used flux tower data from several Fluxnet stations across the country. The classic approach to model evapotranspiration (ET), which is based on the computation of a maximum ET rate and a soil moisture dependent reduction

  3. Using Real-World Case Studies to Advance Hydrology Education in a Changing World

    NASA Astrophysics Data System (ADS)

    Wagener, Thorsten; Reed, Patrick; Zappe, Sarah

    2010-05-01

    Hydrology originated as an engineering discipline mainly concerned with the estimation of floods and droughts. Since then, hydrology has evolved into one of the earth sciences and deals with water related issues in complex environmental systems at scales ranging from local to global. Current and future water issues, however, require new inter-disciplinary scientific approaches to provide solutions to engineering problems, often including significant social components. Climate and land use change introduce non-stationarities into the environment that many of the current engineering tools cannot consider, while a growing population continuously increase the stress on available water resources, particularly in less developed countries. Hydrology therefore remains an important part of the general civil and environmental engineering curriculum. However, the changes in the science of hydrology have not yet fully propagated into a changed approach to teaching this important subject in many engineering departments. We present the results of a three-semester long study in which we introduced real world case studies into a large (70-90 students) civil engineering undergraduate class to achieve this change. Over the past several semesters, students have expressed overwhelmingly positive thoughts on the course adjustments made, including the cases and other active learning elements utilized. We show and discuss evidence of the positive impact on student learning due to the closer link between the course material and real-world examples of a changing world.

  4. Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions

    NASA Astrophysics Data System (ADS)

    Feng, Rong; Duan, Wansuo; Mu, Mu

    2016-04-01

    In this paper, we explored potential observing locations (i.e., the sensitive areas) of positive Indian Ocean dipole (IOD) events to advance beyond the winter predictability barrier (WPB) using the geophysical fluid dynamics laboratory climate model version 2p1 (GFDL CM2p1). The sensitivity analysis is conducted through perfect model predictability experiments, in which the model is assumed to be perfect and so any prediction errors are caused by initial errors. The results show that the initial errors with an east-west dipole pattern are more likely to result in a significant WPB than spatially correlated noises; the areas where the large values of the dipole pattern initial errors are located have great effects on prediction uncertainties in winter and provide useful information regarding the sensitive areas. Further, the prediction uncertainties in winter are more sensitive to the initial errors in the subsurface large value areas than to those in the surface large value areas. The results indicate that the subsurface large value areas are sensitive areas for advancing beyond the WPB of IOD predictions and if we carry out intensive observations across these areas, the prediction errors in winter may be largely reduced. This will lead to large improvements in the skill of wintertime IOD event forecasts.

  5. Predicting binary merger event rates for advanced LIGO/Virgo

    NASA Astrophysics Data System (ADS)

    Holz, Daniel; Belczynski, Chris; O'Shaughnessy, Richard; Bulik, Tomek; LIGO Collaboration

    2016-03-01

    We discuss estimates of the rates of mergers of binary systems composed of neutron stars and/or stellar mass black holes. We use the StarTrack population synthesis code, and make predictions for the detection rate of compact binary coalescences with the advanced LIGO/Virgo gravitational wave detectors. Because these instruments are sensitive to massive (M > 20M⊙) stellar-mass binary black holes mergers out to high redshift (z > 1), we discuss the cosmological effects which must be taken into account when calculating LIGO detection rates, including a generalization of the calculation of the ``peanut factor'' and the sensitive time-volume.

  6. Predictive Understanding of Seasonal Hydrological Dynamics under Climate and Land Use-Land Cover Change

    NASA Astrophysics Data System (ADS)

    Batra, N.; Yang, Y. E.; Choi, H. I.; Kumar, P.; Cai, X.; Fraiture, C. D.

    2008-12-01

    Water has always been and will continue to be an important factor in agricultural production and any alteration in the seasonal distribution of water availability due to climate and land use-land cover change (LULCC) will significantly impact the future production. To achieve the ecologic, economic and social objectives of sustainability, physical understanding of the linkages between climatic changes, LULCC and hydrological response is required. Aided by satellite data, modeling and understanding of the interactions between physical processes of the climate system and society, more reliable regional LULCC and climate change projections are now available. However, resulting quantitative projection of changes on the regional scale hydrological components at the seasonal time scale are sparse. This study attempts to quantify the seasonal hydrological response due to projected LULCC and climate change scenario of Intergovernmental Panel on Climate Change (IPCC) in different hydro-climatic regions of the world. The Common Land Model (CLM) is used for global assessment of future hydrologic response with the development of a consistent global GIS based database for the surface boundary conditions and meteorological forcing of the model. Future climate change projections are derived from the IPCC Fourth Assessment Report: Working Group I - The Physical Science Basis. The study is performed over nine river basins selected from Asia, Africa and North America to present the broad climatic and landscape controls on the seasonal hydrological dynamics. Future changes in water availability are quite evident from our results based upon changes in the volume and seasonality of precipitation, runoff and evapotranspiration. Severe water scarcity is projected to occur in certain seasons which may not be known through annual estimates. Knowledge of the projected seasonal hydrological response can be effectively used for developing adaptive management strategies for the sustainability

  7. Evaluation of SCaMPR Satellite QPEs for Operational Hydrologic Prediction

    NASA Astrophysics Data System (ADS)

    LEE, H.; Zhang, Y.; Seo, D.; Kitzmiller, D. H.; Kuligowski, R. J.; Corby, R.

    2011-12-01

    National Weather Service (NWS) River Forecast Centers (RFCs) use rain gauge or radar-gauge multi-sensor quantitative precipitation estimates (QPEs) as the primary rainfall input to their operational hydrologic models. In areas with poor radar and rain gauge coverage, satellite-based QPEs are a potential alternative. In this work, we evaluated the utility of satellite-based QPEs produced via the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for operational hydrologic modeling for a set of basins in Texas and Louisiana for the period of 2000-7. First, we assessed the relative accuracy of two sets of SCaMPR QPEs versus gauge-only QPE, with operational multi-sensor QPEs as the reference. One set used only operational polar orbiting satellite microwave input as the predictors, the other included Tropical Rainfall Measuring Mission (TRMM) rain rates in the calibration process. We then performed hydrologic simulations using these QPEs and evaluated the simulations. Results indicated that a) SCaMPR QPEs showed better/worse skill than the gauge-only QPEs in resolving heavy precipitation at 1-h/24-h time intervals in terms of Critical Success Index (CSI); b) SCaMPR QPEs underperformed gauge-only QPEs in simulating flood events; and c) ingesting TRMM rainfall rates helped enhance the hydrologic utility of SCaMPR QPE, by mitigating the positive bias of SCaMPR QPEs, elevating the detection rates of heavy rainfall, and improving the simulation of flood discharge. Our findings suggest that the superior performance of gauge-only QPEs versus SCaMPR in hydrologic simulations is tied to its better accuracy at 24-h scale. The implication of the scale dependence in the relative performance of SCaMPR QPEs to their potential hydrologic utility is discussed.

  8. Flash flood prediction using an un-calibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions

    NASA Astrophysics Data System (ADS)

    Rozalis, Shahar; Morin, Efrat; Yair, Yoav; Price, Colin

    2010-05-01

    Flash floods are one of the most severe natural disasters in Europe in general and in Mediterranean areas in particular. They can cause severe damage to property, infrastructures and loss of human life. The complexity of flash-flood generation processes and their dependency on different factors related to watershed properties and rainfall characteristics make flash flood prediction a difficult task. In this study, as a part of the EU-FLASH project, we use an un-calibrated hydrological model to simulate flow events in a 27 km2 Mediterranean watershed in Israel and to analyze and better understand the various factors affecting them. The model is based on the well-known SCS Curve Number method for rainfall-runoff calculations and on the kinematic wave method for flow routing. Existing data available from maps, GIS and field studies have been used to define model parameters, and no further calibration has been conducted to get a better fit between computed and observed flow data. The model rainfall input was obtained from the high temporal and spatial resolution radar data adjusted to rain gauges. 20 flow events which occurred within the study area along a 15 years period have all been analyzed. The model shows a generally good prediction capability (e.g., r2=0.7 for peak discharge) which is mainly due to the high performance in predicting flash-floods generated by intense, short-lived convective storm events (r2=0.9). A better performance is achieved when considering the flood level; then the model is able to predict all events defined as high level flood events. The degree of urban development was found to have a large effect on runoff amount and peak discharge with higher sensitivity of moderate and low flow events relative to high flows. Flash-flood generation was also found to be very sensitive to the temporal distribution of rain intensity within the specific storm event.

  9. Improving the representation of hydrologic processes in Earth System Models

    NASA Astrophysics Data System (ADS)

    Clark, Martyn P.; Fan, Ying; Lawrence, David M.; Adam, Jennifer C.; Bolster, Diogo; Gochis, David J.; Hooper, Richard P.; Kumar, Mukesh; Leung, L. Ruby; Mackay, D. Scott; Maxwell, Reed M.; Shen, Chaopeng; Swenson, Sean C.; Zeng, Xubin

    2015-08-01

    Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater-surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.

  10. SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing S...

  11. Developing soil erodibility prediction equations for the Rangeland Hydrology and Erosion Model (RHEM)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil erodibility is a key factor for estimating soil erosion using physically based models. In this study, a new parameterization approach for estimating erodibility was developed for the Rangeland Hydrology and Erosion Model (RHEM). The approach uses empirical equations that were developed by apply...

  12. Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

    NASA Technical Reports Server (NTRS)

    Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

    2014-01-01

    This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.

  13. Simplifying a hydrological ensemble prediction system with a backward greedy selection of members - Part 2: Generalization in time and space

    NASA Astrophysics Data System (ADS)

    Brochero, D.; Anctil, F.; Gagné, C.

    2011-11-01

    An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This paper evaluates the generalization ability of a simplification scheme of a 800-member HEPS formed by the combination of 16 lumped rainfall-runoff models with the 50 perturbed members from the European Centre for Medium-range Weather Forecasts (ECMWF) EPS. Tests are made at two levels. At the local level, the transferability of the 9th day hydrological member selection for the other 8 forecast horizons exhibits an 82% success rate. The other evaluation is made at the regional or cluster level, the transferability from one catchment to another from within a cluster of watersheds also leads to a good performance (85% success rate), especially for forecast time horizons above 3 days and when the basins that formed the cluster presented themselves a good performance on an individual basis. Diversity, defined as hydrological model complementarity addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.

  14. Uncertainty in Predicted Neighborhood-Scale Green Stormwater Infrastructure Performance Informed by field monitoring of Hydrologic Abstractions

    NASA Astrophysics Data System (ADS)

    Smalls-Mantey, L.; Jeffers, S.; Montalto, F. A.

    2013-12-01

    Human alterations to the environment provide infrastructure for housing and transportation but have drastically changed local hydrology. Excess stormwater runoff from impervious surfaces generates erosion, overburdens sewer infrastructure, and can pollute receiving bodies. Increased attention to green stormwater management controls is based on the premise that some of these issues can be mitigated by capturing or slowing the flow of stormwater. However, our ability to predict actual green infrastructure facility performance using physical or statistical methods needs additional validation, and efforts to incorporate green infrastructure controls into hydrologic models are still in their infancy stages. We use more than three years of field monitoring data to derive facility specific probability density functions characterizing the hydrologic abstractions provided by a stormwater treatment wetland, streetside bioretention facility, and a green roof. The monitoring results are normalized by impervious area treated, and incorporated into a neighborhood-scale agent model allowing probabilistic comparisons of the stormwater capture outcomes associated with alternative urban greening scenarios. Specifically, we compare the uncertainty introduced into the model by facility performance (as represented by the variability in the abstraction), to that introduced by both precipitation variability, and spatial patterns of emergence of different types of green infrastructure. The modeling results are used to update a discussion about the potential effectiveness of urban green infrastructure implementation plans.

  15. Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework

    NASA Astrophysics Data System (ADS)

    Parasuraman, Kamban; Elshorbagy, Amin

    2008-12-01

    Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modeling. The conventional treatment of uncertainty analysis in hydrologic modeling is to assume a deterministic model structure, and treat its associated parameters as imperfectly known, thereby neglecting the uncertainty associated with the model structure. In this paper, a modeling framework that can explicitly account for the effect of model structure uncertainty has been proposed. The modeling framework is based on initially generating different realizations of the original data set using a non-parametric bootstrap method, and then exploiting the ability of the self-organizing algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The performance of the proposed modeling framework is analyzed with regards to its ability in characterizing the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. McMurray, Alberta. Eddy-covariance-measured actual evapotranspiration is modeled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. Investigating the relation between model complexity, prediction accuracy, and uncertainty, two sets of experiments were carried out by varying the level of mathematical operators that can be used to define the predictand-predictor relationship. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predictand-predictor relationship. The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. Compared to the model parameter uncertainty, the relative contribution of model structure uncertainty to the predictive uncertainty of a model is

  16. A Global and Regional Multi-scale Advanced Prediction System

    NASA Astrophysics Data System (ADS)

    Chen, D.; Xue, J.; Yang, X.; Zhang, H.; Liu, J.; Jin, Z.; Huang, L.; Wu, X.

    With social progress and economic development, the requirement for providing much longer, more detailed and more accurate meteorological forecasting services with higher resolution, including climate, synoptic and meso-scale weather forecasts, and air pollution as well as forest fire warning is increased significantly. On the other hand, to meet all needs of services, the numerical weather prediction models will be- come more and more complicated, and more and more ?huge?. The costs for improve- ment and maintenance will be expensive if several NWP systems are to be developed, improved and maintained at the same time and at the same center! In this paper, a Global and Regional multi-scale Advanced Prediction model System (GRAPS) was designed to meet all needs of short, medium and long range weather forecasts as well as environmental predictions. The main features of the GRAPS model include (1) full latitude-longitude grid points; (2) multi-scale in an unified model; (3) hydrostatic or non hydrostatic hypotheses optionally(4) variable or uniform resolution in option (5) possibility to run in regional or global mode(6) finite difference in the vertical dis- cretization in option (7) semi-implicit and semi-Lagrangian scheme; (8) height terrain- following coordinate; (9) Arakawa-C variable staggering; (10) Cascade-interpolation; (11) quasi-conservation of semi-Lagrangian advection scheme combined Staniforth (1992) and Preistley (1993).

  17. Prediction of Corrosion of Advanced Materials and Fabricated Components

    SciTech Connect

    A. Anderko; G. Engelhardt; M.M. Lencka; M.A. Jakab; G. Tormoen; N. Sridhar

    2007-09-29

    The goal of this project is to provide materials engineers, chemical engineers and plant operators with a software tool that will enable them to predict localized corrosion of process equipment including fabricated components as well as base alloys. For design and revamp purposes, the software predicts the occurrence of localized corrosion as a function of environment chemistry and assists the user in selecting the optimum alloy for a given environment. For the operation of existing plants, the software enables the users to predict the remaining life of equipment and help in scheduling maintenance activities. This project combined fundamental understanding of mechanisms of corrosion with focused experimental results to predict the corrosion of advanced, base or fabricated, alloys in real-world environments encountered in the chemical industry. At the heart of this approach is the development of models that predict the fundamental parameters that control the occurrence of localized corrosion as a function of environmental conditions and alloy composition. The fundamental parameters that dictate the occurrence of localized corrosion are the corrosion and repassivation potentials. The program team, OLI Systems and Southwest Research Institute, has developed theoretical models for these parameters. These theoretical models have been applied to predict the occurrence of localized corrosion of base materials and heat-treated components in a variety of environments containing aggressive and non-aggressive species. As a result of this project, a comprehensive model has been established and extensively verified for predicting the occurrence of localized corrosion as a function of environment chemistry and temperature by calculating the corrosion and repassivation potentials.To support and calibrate the model, an experimental database has been developed to elucidate (1) the effects of various inhibiting species as well as aggressive species on localized corrosion of nickel

  18. Integrated Generation of Long and Medium-Range Ensemble Forcing for Hydrologic Ensemble Prediction

    NASA Astrophysics Data System (ADS)

    Schaake, J.

    2006-12-01

    As a part of the hydrology component of the NOAA CPPA Core Project, the NOAA/NWS Office of Hydrologic Development, together with several River Forecast Centers and other collaborators, has been developing a prototype pre-processor to generate precipitation and temperature forcing for our hydrologic ensemble forecast system. This prototype is now in experimental operation at several RFCs. This presentation provides an overview of the current status and an outline of the strategy to integrate additional functionality to use long- range climate forecast information. The current pre-processor uses (i) short range single value forecasts of precipitation and temperature as prescribed by the RFC and (ii) medium range ensemble mean forecasts from a fixed version of NCEP's GFS ensemble forecast system. The initial focus of the long range forecast strategy is to use ensemble mean forecasts from NCEP's CFS ensemble forecast system. Subsequently, possibilities for using other sources of long range forecast information including forecasts from other models and from empirical statistical methods will be discussed.

  19. Nitrate reduction in geologically heterogeneous catchments--a framework for assessing the scale of predictive capability of hydrological models.

    PubMed

    Refsgaard, Jens Christian; Auken, Esben; Bamberg, Charlotte A; Christensen, Britt S B; Clausen, Thomas; Dalgaard, Esben; Effersø, Flemming; Ernstsen, Vibeke; Gertz, Flemming; Hansen, Anne Lausten; He, Xin; Jacobsen, Brian H; Jensen, Karsten Høgh; Jørgensen, Flemming; Jørgensen, Lisbeth Flindt; Koch, Julian; Nilsson, Bertel; Petersen, Christian; De Schepper, Guillaume; Schamper, Cyril; Sørensen, Kurt I; Therrien, Rene; Thirup, Christian; Viezzoli, Andrea

    2014-01-15

    In order to fulfil the requirements of the EU Water Framework Directive nitrate load from agricultural areas to surface water in Denmark needs to be reduced by about 40%. The regulations imposed until now have been uniform, i.e. the same restrictions for all areas independent of the subsurface conditions. Studies have shown that on a national basis about 2/3 of the nitrate leaching from the root zone is reduced naturally, through denitrification, in the subsurface before reaching the streams. Therefore, it is more cost-effective to identify robust areas, where nitrate leaching through the root zone is reduced in the saturated zone before reaching the streams, and vulnerable areas, where no subsurface reduction takes place, and then only impose regulations/restrictions on the vulnerable areas. Distributed hydrological models can make predictions at grid scale, i.e. at much smaller scale than the entire catchment. However, as distributed models often do not include local scale hydrogeological heterogeneities, they are typically not able to make accurate predictions at scales smaller than they are calibrated. We present a framework for assessing nitrate reduction in the subsurface and for assessing at which spatial scales modelling tools have predictive capabilities. A new instrument has been developed for airborne geophysical measurements, Mini-SkyTEM, dedicated to identifying geological structures and heterogeneities with horizontal and lateral resolutions of 30-50 m and 2m, respectively, in the upper 30 m. The geological heterogeneity and uncertainty are further analysed by use of the geostatistical software TProGS by generating stochastic geological realisations that are soft conditioned against the geophysical data. Finally, the flow paths within the catchment are simulated by use of the MIKE SHE hydrological modelling system for each of the geological models generated by TProGS and the prediction uncertainty is characterised by the variance between the

  20. COLLABORATIVE RESEARCH: TOWARDS ADVANCED UNDERSTANDING AND PREDICTIVE CAPABILITY OF CLIMATE CHANGE IN THE ARCTIC USING A HIGH-RESOLUTION REGIONAL ARCTIC CLIMATE SYSTEM MODEL

    SciTech Connect

    Gutowski, William J.

    2013-02-07

    The motivation for this project was to advance the science of climate change and prediction in the Arctic region. Its primary goals were to (i) develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components and (ii) to perform extended numerical experiments using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. These goals were realized first through evaluation studies of climate system components via one-way coupling experiments. Simulations were then used to examine the effects of advancements in climate component systems on their representation of main physics, time-mean fields and to understand variability signals at scales over many years. As such this research directly addressed some of the major science objectives of the BER Climate Change Research Division (CCRD) regarding the advancement of long-term climate prediction.

  1. Combining natural and man-made DNA tracers to advance understanding of hydrologic flow pathway evolution

    NASA Astrophysics Data System (ADS)

    Dahlke, H. E.; Walter, M. T.; Lyon, S. W.; Rosqvist, G. N.

    2014-12-01

    Identifying and characterizing the sources, pathways and residence times of water and associated constituents is critical to developing improved understanding of watershed-stream connections and hydrological/ecological/biogeochemical models. To date the most robust information is obtained from integrated studies that combine natural tracers (e.g. isotopes, geochemical tracers) with controlled chemical tracer (e.g., bromide, dyes) or colloidal tracer (e.g., carboxilated microspheres, tagged clay particles, microorganisms) applications. In the presented study we explore how understanding of sources and flow pathways of water derived from natural tracer studies can be improved and expanded in space and time by simultaneously introducing man-made, synthetic DNA-based microtracers. The microtracer used were composed of polylactic acid (PLA) microspheres into which short strands of synthetic DNA and paramagnetic iron oxide nanoparticles are incorporated. Tracer experiments using both natural tracers and the DNA-based microtracers were conducted in the sub-arctic, glacierized Tarfala (21.7 km2) catchment in northern Sweden. Isotopic hydrograph separations revealed that even though storm runoff was dominated by pre-event water the event water (i.e. rainfall) contributions to streamflow increased throughout the summer season as glacial snow cover decreased. This suggests that glaciers are a major source of the rainwater fraction in streamflow. Simultaneous injections of ten unique DNA-based microtracers confirmed this hypothesis and revealed that the transit time of water traveling from the glacier surface to the stream decreased fourfold over the summer season leading to instantaneous rainwater contributions during storm events. These results highlight that integrating simultaneous tracer injections (injecting tracers at multiple places at one time) with traditional tracer methods (sampling multiple times at one place) rather than using either approach in isolation can

  2. An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water convergence in dryland riparian ecosystems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    To improve the management strategy of riparian restoration, better understanding of the dynamic of eco-hydrological system and its feedback between hydrological and ecological components are needed. The fully distributed eco-hydrological model coupled with a hydrology component was developed based o...

  3. Prediction of Coupled Thermal, Hydrological and Chemical Processes at the Proposed Yucca Mountain Nuclear Waste Repository: An Integrated Approach

    SciTech Connect

    N. Spycher; E. Sonnenthal; T. Kneafsey; P. Dobson

    2003-10-17

    An integrated modeling approach was developed to investigate long-term coupled thermal, hydrological, and chemical (THC) processes that could take place around nuclear waste emplacement tunnels (drifts). The approach involves the development of process models, followed by numerical implementation and validation against field and laboratory experiments before conducting long-term predictive simulations. An outcome of this work was the refinement and validation of an existing reactive transport numerical code for applications specific to the geologic storage of nuclear waste. The model was applied to the case of the proposed high-level nuclear waste repository at Yucca Mountain, Nevada, to evaluate the chemistry of waters potentially seeping into drifts and the effect of water-rock interaction on long-term hydrological behavior around the repository. At liquid saturations significantly larger than residual, no extreme pH or salinity values were predicted. Mineral precipitation around drifts consists mainly of silica with minor calcite, trace zeolites and clays. The effect of mineral precipitation on flow depends largely on initial fracture porosity, and results in negligible to significant diversion of percolation around the drift. Further analyses of model uncertainty are under way to improve confidence in model results.

  4. Predicting the Hydrologic Response of the Columbia River System to Climate Change

    NASA Astrophysics Data System (ADS)

    Chegwidden, O.; Hamman, J.; Xiao, M.; Ishottama, F.; Lee, S. Y.; Stumbaugh, M. R.; Mote, P.; Lettenmaier, D. P.; Nijssen, B.

    2014-12-01

    The Columbia River, located in the northwestern United States with headwaters in Canada (Pacific Northwest), is intensely managed for hydropower generation, irrigation, flood control, ecosystem services (particularly salmonids), navigation, and recreation. Effects of anthropogenic climate change already manifest themselves in the Pacific Northwest through reduced winter snow accumulation at lower elevations and earlier spring melt. As the climate warms, the Columbia River, whose flow regime is heavily dependent on seasonal snow melt, is likely to experience significant changes in the timing of its seasonal hydrograph and possibly in total flow volume. We report on a new study co-funded by the Bonneville Power Administration to update and enhance an existing climate change streamflow data set developed by the University of Washington Climate Impacts Group in 2009-2010. Our new study is based on the RCP4.5 and RCP8.5 climate projections from the Coupled Model Intercomparison Project Version 5 (CMIP5). In contrast to earlier studies, we are using a suite of three hydrologic models, the Variable Infiltration Capacity (VIC) model, the Unified Land Model and the Precipitation Runoff Modeling System, each implemented at 1/16 degree (~6 km) over the Pacific Northwest. In addition, we will use multiple statistical downscaling methods based on the output from a subset of 10 CMIP5 global climate models (GCMs). The use of multiple hydrologic models, downscaling methods and GCMs is motivated by the need to assess the impact of methodological choices in the modeling process on projected changes in Columbia River flows. We discuss the implementation of the three hydrologic models as well as our development of a glacier model for VIC, which is intended to better represent the effects of climate change on streamflows from the Columbia River headwaters region. Finally, we report on our application of a new auto-calibration method that uses an inverse routing scheme to develop

  5. Testing the hydrological landscape unit classification system and other terrain analysis measures for predicting low-flow nitrate and chloride in watersheds.

    PubMed

    Poor, Cara J; McDonnell, Jeffrey J; Bolte, John

    2008-11-01

    Elevated nitrate concentrations in streamwater are a major environmental management problem. While land use exerts a large control on stream nitrate, hydrology often plays an equally important role. To date, predictions of low-flow nitrate in ungauged watersheds have been poor because of the difficulty in describing the uniqueness of watershed hydrology over large areas. Clearly, hydrologic response varies depending on the states and stocks of water, flow pathways, and residence times. How to capture the dominant hydrological controls that combine with land use to define streamwater nitrate concentration is a major research challenge. This paper tests the new Hydrologic Landscape Regions (HLRs) watershed classification scheme of Wolock and others (Environmental Management 34:S71-S88, 2004) to address the question: Can HLRs be used as a way to predict low-flow nitrate? We also test a number of other indexes including inverse-distance weighting of land use and the well-known topographic index (TI) to address the question: How do other terrain and land use measures compare to HLR in terms of their ability to predict low-flow nitrate concentration? We test this for 76 watersheds in western Oregon using the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program and Regional Environmental Monitoring and Assessment Program data. We found that HLRs did not significantly improve nitrate predictions beyond the standard TI and land-use metrics. Using TI and inverse-distance weighting did not improve nitrate predictions; the best models were the percentage land use-elevation models. We did, however, see an improvement of chloride predictions using HLRs, TI, and inverse-distance weighting; adding HLRs and TI significantly improved model predictions and the best models used inverse-distance weighting and elevation. One interesting result of this study is elevation consistently predicted nitrate better than TI or the hydrologic classification

  6. Identifying the simplest predictive model of annual runoff ratio for quantifying the hydrologic impact of climate change in a Great Lakes river basin

    NASA Astrophysics Data System (ADS)

    Meissner, R.; Shaw, S. B.

    2014-12-01

    A standard approach to predict hydrologic fluxes in a changing climate is to downscale climate model output and feed it into a process-based hydrologic model. However, it has been demonstrated that 1) uncertainty in climate model projections often overwhelms uncertainties in the structure and parameterization of hydrologic models and 2) multiple parameter sets and structures can be used to make a hydrologic model match historical discharge (implying actual processes are not always known). Thus, it makes sense to attempt to use the simplest hydrologic model possible, both to focus on better quantifying uncertainty in climate model input and to try to ensure that the described hydrologic processes can actually be confirmed with some confidence. In addition, the use of a simple model increases the transparency of the science and can be helpful in building public consensus in making decisions regarding climate adaptation. As a case study, the Genesee River watershed was examined. The Genesee River is a 6475 sq km watershed extending from Northern Pennsylvania to Lake Ontario, running south to north. We focused on an important but simple measure of hydrologic function: the annual runoff ratio (annual average streamflow/annual average precipitation). The annual runoff ratio varies between 0.3 and 0.8 from 1927 to 2013. The obvious explanation for higher runoff ratio years - high precipitation in seasons with little evapotranspiration - does not readily explain the variations. Three simple attempts to explain this variation were explored: a statistical regression model, the Budyko Curve, and a hydrologic "bucket" model. All models were compared based on ability to replicate annual variations in runoff ratios. Preliminary analyses suggest that although the "bucket" model most closely predicts the annual runoff ratio values, none of the simple models sufficiently explain the annual runoff ratio variability. This indicates that a more complicated hydrologic model may be

  7. Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use

    NASA Astrophysics Data System (ADS)

    Dutta, D.; Kim, S.; Vaze, J.; Hughes, J.

    2015-06-01

    Streamflow in a regulated river system is highly influenced by storage regulations and anthropogenic water use in addition to climate variability. Thus, changes in climate-streamflow relationships and dominant hydrological processes over time are difficult to quantify in a regulated system without partitioning influence of storage regulation and anthropogenic water uses. This requires a robust regulated river system model, which takes into consideration of both hydrological and man-made flow regulation processes, as well as anthropogenic water uses. In this study, a newly developed large-scale river system model (called "AWRA-R") was used to assess the influence of both anthropogenic and climate variability/change on streamflow non-stationarity in the Murray Darling Basin (MDB). MDB is one of the highly regulated basins in Australia with multiple large and small storages developed primarily for supplying water to irrigated agriculture. The modelling was undertaken for the period of 1950-2010, which includes rapid water resources development and both wet and dry climate. The AWRA-R model was calibrated for a reasonably long period and then, validated on an independent period. The calibrated parameters were used to simulate streamflow under current and pre-development conditions to analyse the streamflow variability and influence of climate variability and anthropogenic development on streamflow trend. This paper briefly introduces the model and the method used for assessing streamflow variability under natural and developed conditions and presents the results and findings.

  8. Advanced karst hydrological and contaminant monitoring techniques for real-time and high resolution applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In telogenetic and soil-mantled karst aquifers, the movement of autogenic recharge through the epikarstic zone and into the regional aquifer can be a complex process and have implications for flooding, groundwater contamination, and other difficult to capture processes. Recent advances in instrument...

  9. Prediction of soil stability and erosion in semiarid regions using numerical hydrological model (MCAT) and airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Wittenberg, Lea

    2015-04-01

    Long-term environmental monitoring is addressed to identify physical and biological changes and progresses taking place in the ecosystem. This basic action of landscape monitoring is an essential part of the systematic long-term surveillance, aiming to evaluate, assess and predict the spatial change and progresses. Indeed, it provides a context for wide range of diverse studies and research frameworks from regional or global scale. Spatial-temporal trends and changes at various scales (massive to less certain) require establishing consistent baseline data over time. One of the spatial cases of landscape monitoring is dedicated to soil formation and pedological progresses. It is previously acknowledged that changes in soil affect the functionality of the environment, so monitoring changes recently become important cause considerable resources in areas such as environmental management, sustainability services, and protecting the environment healthy. Given the above, it can be concluded that monitoring changes in the base for sustainable development. The hydrological response of bare soils and watersheds in semiarid regions to intense rainfall events is known to be complex due to multiply physical and structural impacts and feedbacks. As a result, the comprehensive evaluations of mathematical models including detailed consideration of uncertainties in the modeling of hydrological and environmental systems are of increasing importance. The presented method incorporates means of remote sensing data, hydrological and climate data and implementing dedicated and integrative Monte Carlo Analysis Toolbox (MCAT) model for semiarid region. Complexity of practical models to represent spatial systems requires an extensive understanding of the spatial phenomena, while providing realistic balance of sensitivity and corresponding uncertainty levels. Nowadays a large number of dedicated mathematical models applied to assess environmental hydrological process. Among the most

  10. Using Small Unmanned Aerial Systems to Advance Hydrological Models in Coastal Watersheds

    NASA Astrophysics Data System (ADS)

    Moorhead, R.; Hathcock, L.; Coffey, J. J.; Hood, R. E.; van Cooten, S.; Choate, K.; Rawson, H.; Kosturock, A.

    2014-12-01

    Small unmanned aerial systems (sUASs) have the potential to provide highly useful information for models of earth systems that vary over time intervals of days and for which sub-meter resolution is crucial. In particular, the state of coastal watershed plains are highly dependent on vegetation type and cover, soil type, weather, river flooding, and coastal inundation. The vegetation type and cover affect the drying potential, as well as the watershed's resistance to flood water movement. The soil type, soil moisture, and pond depths affect the ability of the watershed to absorb river flood waters and inundation from the sea. In this presentation we will describe a data collection campaign and model modification effort for hydrological models in a coastal watershed. The data collection campaign is obtaining data bimonthly using multiple UASs to capture the state of the watershed quicker. In particular, the vegetation cover and the extent of the water surface expression are captured at approximately a 1 inch spatial resolution over a few days with sUASs that can image 1-2 square miles per hour. The vegetation data provides a time-varying input to improve the estimation of the roughness coefficient and the dry potential from the traditionally static datasets. By correlating the high spatio-temporal resolution surface water expression with data from approximately ten river gauges, models can be improved and validated under more conditions. The presentation will also discuss the requisite sUAS capabilities and our experience in using them.

  11. Prediction of concurrent chemoradiotherapy outcome in advanced oropharyngeal cancer

    PubMed Central

    HASEGAWA, MASAHIRO; MAEDA, HIROYUKI; DENG, ZEYI; KIYUNA, ASANORI; GANAHA, AKIRA; YAMASHITA, YUKASHI; MATAYOSHI, SEN; AGENA, SHINYA; TOITA, TAKAFUMI; UEHARA, TAKAYUKI; SUZUKI, MIKIO

    2014-01-01

    The aim of this study was to investigate human papillomavirus (HPV) infection as a predictor of concurrent chemoradiotherapy (CCRT) response and indicator of planned neck dissection (PND) for patients with advanced oropharyngeal squamous cell carcinoma (OPSCC; stage III/IV). Overall, 39 OPSCC patients (32 men, 7 women; median age 61 years, range 39–79 years) were enrolled. The primary lesion and whole neck were irradiated up to 50.4 Gy, and subsequently the primary site and metastatic lymph nodes were boosted with a further 16.2 Gy. Although several chemotherapy regimens were employed, 82.1% of OPSCC patients received the combination of nedaplatin and 5-fluorouracil. HPV-related OPSCC (16 cases) was defined as both HPV DNA-positive status by polymerase chain reaction and p16INK4a overexpression by immunohistochemistry. Patients with N2 and N3 disease received PND 2–3 months after CCRT completion. Compared to non-responders, CCRT responders showed significantly lower nodal stage (N0 to N2b) and HPV-positive status in univariate analysis. Patients with HPV-related OPSCC had longer time to treatment failure (TTF) than those with HPV-unrelated OPSCC (p=0.040). Three-year TTF was 81.3 and 47.8% in the HPV-related and HPV-unrelated groups, respectively. There were also significant differences in disease-free survival (DFS) between the two OPSCC patient groups (p=0.042). Three-year DFS was 93.8 and 66.7% in patients with HPV-related and HPV-unrelated OPSCC, respectively. Multivariate logistic analysis showed a lower risk of TTF event occurrence in HPV-related OPSCC (p=0.041) than in HPV-unrelated OPSCC. Thus, HPV testing in addition to nodal stage was useful for predicting CCRT response, especially in advanced OPSCC. Because patients who received PND showed moderate locoregional control, PND is an effective surgical procedure for controlling neck lesions in patients with advanced HPV-unrelated disease. PMID:24969413

  12. Using Advances in Research on Louisiana Coastal Restoration and Protection to Develop Undergraduate Hydrology Education Experiences Delivered via a Web Interface

    NASA Astrophysics Data System (ADS)

    Bodin, M.; Habib, E. H.; Meselhe, E. A.; Visser, J.; Chimmula, S.

    2014-12-01

    Utilizing advances in hydrologic research and technology, learning modules can be developed to deliver visual, case-based, data and simulation driven educational experiences. This paper focuses on the development of web modules based on case studies in Coastal Louisiana, one of three ecosystems that comprise an ongoing hydrology education online system called HydroViz. The Chenier Plain ecosystem in Coastal Louisiana provides an abundance of concepts and scenarios appropriate for use in many undergraduate water resource and hydrology curricula. The modules rely on a set of hydrologic data collected within the Chenier Plain along with inputs and outputs of eco-hydrology and vegetation-change simulation models that were developed to analyze different restoration and protection projects within the 2012 Louisiana Costal Master Plan. The modules begin by investigating the basic features of the basin and it hydrologic characteristics. The eco-hydrology model is then introduced along with its governing equations, numerical solution scheme and how it represents the study domain. Concepts on water budget in a coastal basin are then introduced using the simulation model inputs, outputs and boundary conditions. The complex relationships between salinity, water level and vegetation changes are then investigated through the use of the simulation models and associated field data. Other student activities focus on using the simulation models to evaluate tradeoffs and impacts of actual restoration and protection projects that were proposed as part of 2012 Louisiana Master Plan. The hands-on learning activities stimulate student learning of hydrologic and water management concepts by providing real-world context and opportunity to build fundamental knowledge as well as practical skills. The modules are delivered through a carefully designed user interface using open source and free technologies which enable wide dissemination and encourage adaptation by others.

  13. Advances in Remote Sensing for Assessing High Altitude Glacio-Hydrology - with a Focus on High Mountain Asia

    NASA Astrophysics Data System (ADS)

    Bolch, T.

    2014-12-01

    Meltwater released by glaciers can be of high importance for the overall run-off and thus affect society and development of mountainous regions and their forelands. However, glaciers are mostly located in harsh and remote environment and detailed in-situ measurements are impossible or limited to few glaciers. This lack of measurements of glacier characteristics (e.g. area, debris cover, flow) and mass budgets hampers a correct glacio-hydrologic modelling and representation of processes in advanced simulation models. Remote sensing has been proven a powerful tool in providing essential data to fill this gap. The most basic information in this respect is the location and area of the glaciers. A global and some regional inventories exist, but the uncertainties and differences among them are high, especially with respect to the upper accumulation area and debris cover. I here present a multi-method approach to map glaciers more precisely based on remote sensing data and combining image ratioing (using visible, infrared and thermal bands), micro-wave coherence images, terrain analysis, differencing of digital elevation models (DEMs) and, if available, high resolution images. DEM differencing is used to provide region-wide mass balance assessments, but volume to mass conversion and data voids introduce uncertainties. For High Mountain Asia (HMA), a crucial region in terms of water resources and glacier changes, most studies concentrate on the period after the year 2000 with the SRTM-DEM as baseline data set. However, declassified satellite data from the 1960s and 1970s also exist and allowed to extend the data record back in time for several regions in HMA. Using an example from an ice-covered area of ~5000 km² in the Aksu-Tarim catchment in Central Tien Shan the importance of remote sensing for glacio-hydrological modelling is shown. This is especially true for debris-covered and surge-type glaciers whose reaction to climate is still not fully understood. Therefore

  14. Simplifying a hydrological ensemble prediction system with a backward greedy selection of members - Part 2: Generalization in time and space

    NASA Astrophysics Data System (ADS)

    Brochero, D.; Anctil, F.; Gagné, C.

    2011-03-01

    An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This article evaluates a 94% simplification of an initial 800-member HEPS, forcing 16 lumped rainfall-runoff models with the European Center for Medium-range Weather Forecasts (ECMWF MEPS). More specifically, it tests the time (local) and space (regional) generalization ability of the simplified 50-member HEPS obtained using a methodology that combines 4 main aspects: (i) optimizing information of the short-length series using k-folds cross-validation, (ii) implementing a backward greedy selection technique, (iii) guiding the selection with a linear combination of diversified scores, and (iv) formulating combination case studies at the cross-validation stage. At the local level, the transferability of the 9th day member selection was proven for the other 8 forecast horizons at an 82% success rate. At the regional level, a good performance was also achieved when the 50-member HEPS was applied to a neighbouring catchment within the same cluster. Diversity, defined as hydrological model complementarities addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.

  15. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis

    NASA Astrophysics Data System (ADS)

    Thyer, Mark; Renard, Benjamin; Kavetski, Dmitri; Kuczera, George; Franks, Stewart William; Srikanthan, Sri

    2009-12-01

    The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall-runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer, and evaluate probability models describing input, output, and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) to address two key requirements of uncertainty assessment: (1) reliable quantification of predictive uncertainty and (2) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantile-quantile analysis, suggest that while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly dependent on the selected rain gauge and calibration period. This will obscure potential relationships between CRR parameters and catchment attributes and prevent the development of meaningful regional relationships. Conversely, BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization. However, significant departures from the calibration assumptions remained even in BATEA, e.g., systematic overestimation of predictive uncertainty, especially in validation. This is likely due to the inferred rainfall errors compensating for simplified treatment of model structural error.

  16. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Cancer.gov

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  17. On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models

    NASA Astrophysics Data System (ADS)

    Kikuchi, C. P.; Ferré, T. P. A.; Vrugt, J. A.

    2015-06-01

    Experimental design and data collection constitute two main steps of the iterative research cycle (aka the scientific method). To help evaluate competing hypotheses, it is critical to ensure that the experimental design is appropriate and maximizes information retrieval from the system of interest. Scientific hypothesis testing is implemented by comparing plausible model structures (conceptual discrimination) and sets of predictions (predictive discrimination). This research presents a new Discrimination-Inference (DI) methodology to identify prospective data sets highly suitable for either conceptual or predictive discrimination. The DI methodology uses preposterior estimation techniques to evaluate the expected change in the conceptual or predictive probabilities, as measured by the Kullback-Leibler divergence. We present two case studies with increasing complexity to illustrate implementation of the DI for maximizing information withdrawal from a system of interest. The case studies show that highly informative data sets for conceptual discrimination are in general those for which between-model (conceptual) uncertainty is large relative to the within-model (parameter) uncertainty, and the redundancy between individual measurements in the set is minimized. The optimal data set differs if predictive, rather than conceptual, discrimination is the experimental design objective. Our results show that DI analyses highlight measurements that can be used to address critical uncertainties related to the prediction of interest. Finally, we find that the optimal data set for predictive discrimination is sensitive to the predictive grouping definition in ways that are not immediately apparent from inspection of the model structure and parameter values.

  18. Predicting phosphorus dynamics in complex terrains using a variable source area hydrology model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Prediction of phosphorus (P) loss from agricultural watersheds depends on accurately representing the processes governing P loss from agricultural watersheds and the particular transport factors facilitating loss. The Soil and Water Assessment Tool (SWAT), a model commonly used to predict runoff an...

  19. Life prediction methodology for ceramic components of advanced heat engines. Phase 1: Volume 1, Final report

    SciTech Connect

    Cuccio, J.C.; Brehm, P.; Fang, H.T.

    1995-03-01

    Emphasis of this program is to develop and demonstrate ceramics life prediction methods, including fast fracture, stress rupture, creep, oxidation, and nondestructive evaluation. Significant advancements were made in these methods and their predictive capabilities successfully demonstrated.

  20. A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

    NASA Astrophysics Data System (ADS)

    Niedzielski, Tomasz; Mizinski, Bartlomiej

    2016-04-01

    The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

  1. New Approaches to Assessing and Predicting the Hydrologic Impacts of Urban Disturbance Using Isotopes and Transit Time Analysis

    NASA Astrophysics Data System (ADS)

    Soulsby, C.; Geris, J.; Birkel, C.; Tetzlaff, D.

    2015-12-01

    Urbanization is an abrupt hydrological disturbance that affects large parts of the world. For ameliorative management, an understanding of how flow partitioning and storage dynamics are affected is crucial, yet this remains limited. This reflects the lack of integrated monitoring and modelling frameworks for characterizing these hydrological response dynamics to incremental urban development. Here we use a coupled flow-isotope model to assess the impacts of urbanisation (~20%) on stream water age distributions in an 8 km2 catchment. A conceptual runoff model was used with flux tracking to estimate the time-varying age of stream water at the outlet and both urban and non-urban sub-catchments over a 3 year period. Combined objective functions of both flow and isotope metric constrained model structures, improved calibration and aided model evaluation. Specifically, we explored (1) the age distribution of stream water draining urban and non-urban areas, (2) the integrated effect of these different land uses at larger catchment scales, and (3) how the modelling can predict the impacts on the stream water age of future urbanization proposals. The results showed that stream water draining the most urbanized tributary was youngest with a mean transit time (MTT) of < 6 months compared with ~18 months in the non-urban tributary. For the catchment outlet, the MTT was around 9 months. Here, the response of urban areas dominated smaller and moderate events, but rural contributions dominated during the wettest periods, giving a bi-modal distribution of water ages. Predictions for planned developments in the area indicated that just a 5% increase in urban area would give dramatic reductions in MTTs that can propagate to the larger catchment scale. This novel approach offers a framework for understanding the cumulative impacts of disturbances on streams. It can also contribute to the design of more sustainable urban water design in terms of targeted restriction of rapid water

  2. An Efficient Scaling Technique For Predicting Fine Resolution Terrestrial Hydrologic And Carbon Dynamics

    NASA Astrophysics Data System (ADS)

    Pau, G. S. H.; Shen, C.; Riley, W. J.

    2015-12-01

    Limited computational resources typically limit the spatial resolution of watershed-scale hydrologic-biogeochemical models, thus necessitating the upscaling of topographic, biotic, and abiotic parameters. Upscaled models, however, may not capture nonlinear interactions between processes accurately, leading to significant biases in the solutions. In this presentation, we consider a sampling-based scaling approach that maps coarse-resolution solutions to the high-resolution solutions. The approach, called Proper Orthogonal Decomposition Mapping Method (PODMM), trains a reduced order model with coarse- and fine- solutions, here obtained using a high-resolution watershed-scale model. The model, PAWS+CLM, is a quasi-3D watershed model that has been validated against observations in many temperate watersheds; here we applied PAWS+CLM to construct coarse- (7 km) and fine- (200m) resolution simulations for the Clinton River basin in Michigan, USA. After the training stage, subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM, leading to significant computational speedup. We demonstrate that fine-resolution soil moisture, latent heat flux, and net primary production are accurately reproduced, with up to 80% bias reduction compared to solutions obtained using coarse-resolution model. In addition, the subgrid distributions of the ROM solutions closely resemble those obtained using the fine-resolution model. This method can potentially bridge the intrinsic difference in scales between different processes by allowing efficient upscaling and downscaling of spatial solutions.

  3. A retrospective assessment of National Centers for Environmental Prediction climate model-based ensemble hydrologic forecasting in the western United States

    NASA Astrophysics Data System (ADS)

    Wood, Andrew W.; Kumar, Arun; Lettenmaier, Dennis P.

    2005-02-01

    We assess the potential forecast skill of a climate model-based approach for seasonal ensemble hydrologic and streamflow forecasting for the western United States. By using climate model ensemble forecasts and ensembles formed via the resampling of observations, we distinguish hydrologic forecast skill resulting from the predictable evolution of initial hydrologic conditions from that derived from the climate model forecasts. Monthly climate model ensembles of precipitation and temperature produced by the National Centers for Environmental prediction global spectral model (GSM) are downscaled for use as forcings of the variable infiltration capacity (VIC) hydrologic model. VIC then simulates ensembles of streamflow and spatially distributed hydrologic variables such as snowpack, soil moisture, and runoff. The regional averages of the ensemble forcings and derived hydrologic variables were evaluated over five regions: the Pacific Northwest, California, the Great Basin, the Colorado River basin, and the upper Rio Grande River basin. The skill assessment focuses on a retrospective 21-year period (1979-1999) during which GSM retrospective forecast ensembles (termed hindcasts), created using similar procedures to GSM real-time forecasts, are available. The observational verification data set for the hindcasts was a retrospective hydroclimatology at 1/8°-1/4° consisting of gridded observations of temperature and precipitation and gridded hydrologic simulation results (for hydrologic variables and streamflow) based on the observed meteorological inputs. The GSM hindcast skill was assessed relative to that of a naive ensemble climatology forecast and to that of ensemble streamflow prediction (ESP) hindcasts, a forecast baseline sharing the same initial condition information as the GSM-based hindcasts. We found that the unconditional (all years) GSM hindcasts for regionally averaged variables provided practically no skill improvement over the ESP hindcasts and did not

  4. Neighbourhood selection for local modelling and prediction of hydrological time series

    NASA Astrophysics Data System (ADS)

    Jayawardena, A. W.; Li, W. K.; Xu, P.

    2002-02-01

    The prediction of a time series using the dynamical systems approach requires the knowledge of three parameters; the time delay, the embedding dimension and the number of nearest neighbours. In this paper, a new criterion, based on the generalized degrees of freedom, for the selection of the number of nearest neighbours needed for a better local model for time series prediction is presented. The validity of the proposed method is examined using time series, which are known to be chaotic under certain initial conditions (Lorenz map, Henon map and Logistic map), and real hydro meteorological time series (discharge data from Chao Phraya river in Thailand, Mekong river in Thailand and Laos, and sea surface temperature anomaly data). The predicted results are compared with observations, and with similar predictions obtained by using arbitrarily fixed numbers of neighbours. The results indicate superior predictive capability as measured by the mean square errors and coefficients of variation by the proposed approach when compared with the traditional approach of using a fixed number of neighbours.

  5. Predicted hydrologic effects of pumping from the Lichterman Well Field in the Memphis Area, Tennessee

    USGS Publications Warehouse

    Nyman, Dale J.

    1965-01-01

    The Lichterman well field is scheduled to go into operation early in 1965 to supplement the municipal water-supply system for the city of Memphis, Tenn. Although the initial rate of withdrawal from the well field will be about 8 mgd (million gallons per day), the ultimate design capacity of the field is 20 mgd. A study of sand samples, drillers' logs, and geophysical logs collected during preliminary test drilling at the site for the Lichterman well field was used as a basis for defining three zones of sand favorable for the construction of high-capacity (1,000 gallons per minute or more) water wells. The three zones occur in the '500-foot' sand and are here designated (in descending order) as zone A, zone B, and zone C. The depth to the top of these zones below land surface has the following ranges: zone A, 125 to 225 feet; zone B, 200 to 350 feet; and zone C, 700 to 775 feet. Zones A and B range from 0 to 100 feet in thickness, and zone C ranges from 10 to 100 feet in thickness. Within the well field proper these zones are expected to react to the stress of pumping as separate hydrologic units, but outside the well field the three zones are expected to react as a single hydrologic unit. The '500-foot' sand in the Germantown-Collierville area is recharged chiefly by precipitation on the outcrop area of the sand to the east, but the evidence indicates that additional recharge is entering the aquifer from the Wolf River. In spite of this additional recharge, water levels in the '500-foot' sand are declining at an average rate of about two-thirds of a foot per year, owing to municipal and industrial pumpage in the Memphis area. However, this decline is not expected to alter the excellent quality of the water in the '500-foot' sand at the site of the Lichterman well field. Pumping in the Lichterman well field will create a cone of depression in the free-water (piezometric) surface of the '500-foot' sand. The decline in water levels will be directly proportional to the

  6. 76 FR 52954 - Workshop: Advancing Research on Mixtures; New Perspectives and Approaches for Predicting Adverse...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-24

    ... HUMAN SERVICES Workshop: Advancing Research on Mixtures; New Perspectives and Approaches for Predicting... ``Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects... Research and Training, NIEHS, P.O. Box 12233, MD K3-04, Research Triangle Park, NC 27709, (telephone)...

  7. Leveraging simultaneous SMOS and ASCAT soil moisture products for enhanced hydrologic prediction

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Runoff predictions obtained from rainfall runoff model are typically degraded for a wide variety of error sources including the inaccurate specification of pre-storm soil moisture conditions (determining infiltration capacity) and random error in rainfall inputs (especially in areas of a world lacki...

  8. Hydrologic modeling as a predictive basis for ecological restoration of salt marshes

    USGS Publications Warehouse

    Roman, C.T.; Garvine, R.W.; Portnoy, J.W.

    1995-01-01

    Roads, bridges, causeways, impoundments, and dikes in the coastal zone often restrict tidal flow to salt marsh ecosystems. A dike with tide control structures, located at the mouth of the Herring River salt marsh estuarine system (Wellfleet, Massachusetts) since 1908, has effectively restricted tidal exchange, causing changes in marsh vegetation composition, degraded water quality, and reduced abundance of fish and macroinvertebrate communities. Restoration of this estuary by reintroduction of tidal exchange is a feasible management alternative. However, restoration efforts must proceed with caution as residential dwellings and a golf course are located immediately adjacent to and in places within the tidal wetland. A numerical model was developed to predict tide height levels for numerous alternative openings through the Herring River dike. Given these model predictions and knowledge of elevations of flood-prone areas, it becomes possible to make responsible decisions regarding restoration. Moreover, tidal flooding elevations relative to the wetland surface must be known to predict optimum conditions for ecological recovery. The tide height model has a universal role, as demonstrated by successful application at a nearby salt marsh restoration site in Provincetown, Massachusetts. Salt marsh restoration is a valuable management tool toward maintaining and enhancing coastal zone habitat diversity. The tide height model presented in this paper will enable both scientists and resource professionals to assign a degree of predictability when designing salt marsh restoration programs.

  9. Recent advances towards a theory of catchment hydrologic transport: age-ranked storage and the Ω-functions

    NASA Astrophysics Data System (ADS)

    Harman, C. J.

    2014-12-01

    Models that faithfully represent spatially-integrated hydrologic transport through the critical zone at sub-watershed scales are essential building blocks for large-scale models of land use and climate controls on non-point source contaminant delivery. A particular challenge facing these models is the need to represent the delay between inputs of soluble contaminants (such as nitrate) at the field scale, and the solute load that appears in streams. Recent advances in the theory of time-variable transit time distributions (e.g. Botter et al., GRL 38(L11403), 2011) have provided a rigorous framework for representing conservative solute transport and its coupling to hydrologic variability and partitioning. Here I will present a reformulation of this framework that offers several distinct advantages over existing formulations: 1) the derivation of the governing conservation equation is simple and intuitive, 2) the closure relations are expressed in a convenient and physically meaningful way as probability distributions Ω(ST)Omega(S_T) over the storage ranked by age STS_T, and 3) changes in transport behavior determined by storage-dependent dilution and flow-path dynamics (as distinct from those due only to changes in the rates and partitioning of water flux) are completely encapsulated by these probability distributions. The framework has been implemented to model to the rich dataset of long-term stream and precipitation chloride from the Plynlimon watershed in Wales, UK. With suitable choices for the functional form of the closure relationships, only a small number of free parameters are required to reproduce the observed chloride dynamics as well as previous models with many more parameters, including reproducing the observed fractal 1/f filtering of the streamflow chloride variability. The modeled transport dynamics are sensitive to the input precipitation variability and water balance partitioning to evapotranspiration. Apparent storage-dependent age

  10. Recent advances in predictability studies in China (1999-2002)

    NASA Astrophysics Data System (ADS)

    Mu, Mu; Wansuo, Duan; Jifan, Chou

    2004-06-01

    Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed, which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealed by NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate, which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition, in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance

  11. [Research advance in the drug target prediction based on chemoinformatics].

    PubMed

    Fang, Jian-song; Liu, Ai-lin; Du, Guan-hua

    2014-10-01

    The emerging of network pharmacology and polypharmacology forces the scientists to recognize and explore new mechanisms of existing drugs. The drug target prediction can play a key significance on the elucidation of the molecular mechanism of drugs and drug reposition. In this paper, we systematically review the existing approaches to the prediction of biological targets of small molecule based on chemoinformatics, including ligand-based prediction, receptor-based prediction and data mining-based prediction. We also depict the strength of these methods as well as their applications, and put forward their developing direction. PMID:25577863

  12. An integrated tool for real time prediction of hydrological response of steep-slopes in shallow pyroclastic deposits

    NASA Astrophysics Data System (ADS)

    Damiano, E.; Giorgio, M.; Greco, R.; Guida, A.; Netti, N.; Olivares, L.; Savastano, V.

    2012-04-01

    A large part of the mountains of Campania, in southern Italy, are interested by catastrophic flowslides triggered by heavy rainfalls. The slopes are covered by shallow deposits of loose pyroclastic soils in unsaturated conditions, which equilibrium is assured by the contribution of apparent cohesion due to soil suction. Hence, a key tool for the prediction of slope stability is the short-term forecasting of intense and persistent rainfall events and the subsequent analysis of the hydrological response of the shallow covers during such events. To this aim a numerical tool, is presented, consisting of a module for stochastic short-term rainfall prediction and 3D finite volumes model of infiltration and seepage through porous medium, provided with a geotechnical module for slope stability analysis. The presented predictor of rainfall evolution consists of an event based stochastic model, allowing formulating real time predictions of the future evolution of a storm, conditioned to the observed part of the storm. The 3D code (I-MOD3D) was calibrated through back-analysis of infiltration tests on slope model (Olivares et al. 2009) and of in situ suction measurements (Olivares and Damiano, 2007) collected in a instrumented site on a slope where recently a catastrophic flowslide occurred. The calibrated model has been applied to real time predictions of the slope response during some observed storms, showing the reliability of the results of the proposed model, which may represent a useful tool for decision making to implement early warning systems. Olivares L. and Damiano E. (2007). Post-failure mechanics of landslides: laboratory investigation of flowslides in pyroclastic soils. Journal of Geotechnical and Geoenvironmental Engineering ASCE, 133(1): 51-62 Olivares L., Damiano E, Greco R, Zeni L, Picarelli L, Minardo A, Guida A, Bernini R (2009). An Instrumented Flume to Investigate the Mechanics of Rainfall-Induced Landslides in Unsaturated Granular Soils. GEOTECHNICAL

  13. Uncertainty Analysis of the Ensemble Hydrological Forecasts in the Coupled Meteorological-Hydrological Modelling Environment

    NASA Astrophysics Data System (ADS)

    Xuan, Y.; Cluckie, I. D.

    2006-12-01

    The advances in meso-scale numerical weather predication render hydrologists the capability to incorporate high-resolution NWP directly into flood forecasting systems in order to obtain an extended lead time. However, such a direct application of rainfall outputs from the NWP model can contribute considerable uncertainties to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be highlighted by the scaling process. In this research, the ensemble hydrological forecasts driven by the ensemble weather prediction are investigated in an effort trying to understand both the potential and the implication of the ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. A data-rich catchment facilitated with dense rainguage network as well as high resolution weather radar was chosen to run the ensemble hydrological simulations of a distributed hydrological model driven by the high resolution NWP predictions. The uncertainties of the amount and the location/timing of the rainfall prediction are discussed whith the results showing that: (1) the hydrological model driven by the short-range NWP can produce forecasts comparable with those from a raingauge-driven one; (2) the ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematical biases sometimes are significantly large and, as such, extra efforts need to be made to improve the quality of such a system.

  14. Improving the representation of hydrologic processes in Earth System Models

    SciTech Connect

    Clark, Martyn P.; Fan, Ying; Lawrence, David M.; Adam, Jennifer C.; Bolster, Diogo; Gochis, David J.; Hooper, Richard P.; Kumar, Mukesh; Leung, L. Ruby; Mackay, D. Scott; Maxwell, Reed M.; Shen, Chaopeng; Swenson, Sean C.; Zeng, Xubin

    2015-08-21

    Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale models, such as the land components of Earth System Models (ESMs), do not yet represent the terrestrial water cycle in a fully integrated manner or resolve the finer-scale processes that can dominate large-scale water budgets. This paper reviews the current representation of hydrologic processes in ESMs and identifies the key opportunities for improvement. This review suggests that (1) the development of ESMs has not kept pace with modeling advances in hydrology, both through neglecting key processes (e.g., groundwater) and neglecting key aspects of spatial variability and hydrologic connectivity; and (2) many modeling advances in hydrology can readily be incorporated into ESMs and substantially improve predictions of the water cycle. Accelerating modeling advances in ESMs requires comprehensive hydrologic benchmarking activities, in order to systematically evaluate competing modeling alternatives, understand model weaknesses, and prioritize model development needs. This demands stronger collaboration, both through greater engagement of hydrologists in ESM development and through more detailed evaluation of ESM processes in research watersheds. Advances in the representation of hydrologic process in ESMs can substantially improve energy, carbon and nutrient cycle prediction capabilities through the fundamental role the water cycle plays in regulating these cycles.

  15. Three-dimensional prediction of soil physical, chemical, and hydrological properties in a forested catchment of the Santa Catalina CZO

    NASA Astrophysics Data System (ADS)

    Shepard, C.; Holleran, M.; Lybrand, R. A.; Rasmussen, C.

    2014-12-01

    in each cluster calculated. Mass-preserving splines combined with stepwise regressions are an effective tool for predicting soil physical, chemical, and hydrological properties with depth, enhancing our understanding of the critical zone.

  16. Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

    NASA Technical Reports Server (NTRS)

    Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

    2014-01-01

    This presentation describes the capabilities of three-dimensional thermal power model of advanced stirling radioisotope generator (ASRG). The performance of the ASRG is presented for different scenario, such as Venus flyby with or without the auxiliary cooling system.

  17. Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

    NASA Astrophysics Data System (ADS)

    Peters-Lidard, C. D.; Harrison, K. W.; Tian, Y.; Kumar, S.

    2011-12-01

    One of the five scientific objectives for GPM is to "Improve hydrological modeling and prediction", including advancing prediction skill for high-impact hazards such as floods, droughts, landslides and landfalling hurricanes. Given the focus on land hydrology, and the range of hydrologic regimes targeted by GPM, it follows that a hydrologically-oriented ground validation program that covers these regimes from both the physical retrieval and the hydrological prediction perspectives is required for the successful application of GPM to land hydrology. In order to investigate the robustness of both hydrologic model predictions and physical precipitation retrievals, this talk will present recent evaluations of skill in land surface hydrologic models forced with TRMM-era multisensor products, with and without land data assimilation. In addition to LSM skill, we will also demonstrate how physical precipitation retrievals can be supported by land surface emissivity and temperature estimates obtained by coupling microwave emission models (e.g., the Joint Center for Satellite Data Assimilation Community Radiative Transfer Model CRTM and the European Center for Medium-Range Weather Forecasting's Community Microwave Emission Model CMEM) to the land surface models in the Land Information System (LIS; http://lis.gsfc.nasa.gov). Evaluation at multiple frequencies, with and without land data assimilation, demonstrates the critical impact of certain real-time ancillary data (e.g., snow cover) on the microwave background states required for physical retrievals.

  18. Prediction of surface flow hydrology and sediment retention upslope of a vetiver buffer strip

    NASA Astrophysics Data System (ADS)

    Hussein, Janet; Yu, Bofu; Ghadiri, Hossein; Rose, Calvin

    2007-05-01

    SummaryVegetated buffer strips are widely used to reduce fluxes of eroding soil and associated chemicals, from hillslopes into waterways. Sediment retention by buffers is time-dependent, with its effectiveness changing with the deposition process. Our research focuses on settling of sediment upslope of stiff grass buffers at three slopes, under subcritical flow conditions. A new model is developed which couples the hydraulics, sediment deposition and subsequent adjustment to topography in order to predict water and sediment profiles upslope of a buffer with time. Experiments to test the model were carried out in the Griffith University Tilting-Flume Simulated Rainfall facility using subcritical flows at 1%, 3% and 5% slopes. Water and sediment profiles were measured at different times as Vertisol sediment was introduced upslope of a vetiver grass strip. A region of increased flow depth (backwater) was produced upslope of the strip which increased in depth and decreased in length with increasing slope. Backwater height could be predicted from flow rates and thus could be used as an input for the model in the absence of experimental data. As slope increased, sediment was deposited closer to the grass strip, moving into the grass strip itself at 5% slope. The grass strip was less effective in reducing sediment in the outflow as slope increased and differences between slopes were significant. Model prediction of water and sediment profiles compared reasonably well with measured data, giving low root mean square errors and high coefficients of model efficiency. Masses of deposited sediment were generally simulated within 20% of measured values. However, simulated particle size distributions of deposited sediment were less accurate.

  19. Advances and Computational Tools towards Predictable Design in Biological Engineering

    PubMed Central

    2014-01-01

    The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated. PMID:25161694

  20. firestar--advances in the prediction of functionally important residues.

    PubMed

    Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

    2011-07-01

    firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php. PMID:21672959

  1. Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis

    ERIC Educational Resources Information Center

    Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.

    2006-01-01

    Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…

  2. Factors that Predict Who Takes Advanced Courses in Cognitive Therapy

    ERIC Educational Resources Information Center

    Pehlivanidis, Artemios

    2007-01-01

    Training in Cognitive Therapy (CT) includes theoretical and didactic components combined with clinical supervision. An introductory course in CT might satisfy training needs in psychotherapy and help in the selection of those trainees who wish to continue to an advanced training level. Predictors of success at such an introductory course have been…

  3. Assessment of NEXRAD and Rain Gauge Precipitation Data for Hydrological Response Predictions in the St Joseph River Watershed, USA

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Precipitation is a major driving force variable behind all hydrologic processes needed for watershed modeling studies. The use of point-scale rain gauge data in watershed hydrologic models may not effectively capture the spatial distribution of rainfall; thereby, directly affecting the water balance...

  4. Testing a distributed hydrological model to predict scenarios of extreme events on a marginal olive orchard microcatchment

    NASA Astrophysics Data System (ADS)

    Guzmán, Enrique; Aguilar, Cristina; Taguas, Encarnación V.

    2014-05-01

    Olive groves constitute a traditional Mediterranean crop and thus, an important source of income to these regions and a crucial landscape component. Despite its importance, most of the olive groves in the region of Andalusia, Southern Spain, are located in sloping areas, which implies a significant risk of erosion. The combination of data and models allow enhancing the knowledge about processes taking place in these areas as well as the prediction of future scenarios. This aspect might be essential to plan soil protection strategies within a context of climate change where the IPCC estimates a significant increase of soil aridity and torrential events by the end of the century. The objective of this study is to estimate the rainfall-runoff-sediment dynamics in a microcatchment olive grove with the aid of a physically-based distributed hydrological model in order to evaluate the effect of extreme events on runoff and erosion. This study will allow to improve land-use and management planning activities in similar areas. In addition, the scale of the study (microcatchment) will allow to contrast the results in larger areas such as catchment regional spatial scales.

  5. Hydrologic modeling to predict performance of shallow land burial cover designs at the Los Alamos National Laboratory

    SciTech Connect

    Nyhan, J.W.

    1989-03-01

    The water balance relationships of two shallow land burial (SLB) cover configurations were studied using a hydrologic model in a preliminary attempt to design waste disposal site covers for successful long-term closure at Los Alamos. Burial site performance requirements for site closure are first discussed, along with the role of hydrologic models in assessing the dynamics of the hydrology of the SLB cover. The calibration of a hydrologic model using field data from two SLB cover designs is then described, followed by an analysis of long-term climatic model input parameters across Los Alamos National Laboratory. These two calibrated models are then used to evaluate the influence of vegetation, precipitation, and runoff curve number on the design of SLB covers within Los Alamos county. Future directions of field research efforts and subsequent hydrologic modeling activities were recommended in terms of their usefulness for waste management decisions to be made at Los Alamos. 24 refs., 17 figs., 9 tabs.

  6. Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

    NASA Astrophysics Data System (ADS)

    Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

    2015-04-01

    The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

  7. Prediction in cases with superposition of different hydrological phenomena, such as from weather "cold drops

    NASA Astrophysics Data System (ADS)

    Anton, J. M.; Grau, J. B.; Tarquis, A. M.; Andina, D.; Sanchez, M. E.

    2012-04-01

    The authors have been involved in Model Codes for Construction prior to Eurocodes now Euronorms, and in a Drainage Instruction for Roads for Spain that adopted a prediction model from BPR (Bureau of Public Roads) of USA to take account of evident regional differences in Iberian Peninsula and Spanish Isles, and in some related studies. They used Extreme Value Type I (Gumbell law) models, with independent actions in superposition; this law was also adopted then to obtain maps of extreme rains by CEDEX. These methods could be extrapolated somehow with other extreme values distributions, but the first step was useful to set valid superposition schemas for actions in norms. As real case, in East of Spain rain comes usually extensively from normal weather perturbations, but in other cases from "cold drop" local high rains of about 400mm in a day occur, causing inundations and in cases local disasters. The city of Valencia in East of Spain was inundated at 1,5m high from a cold drop in 1957, and the river Turia formerly through that city was just later diverted some kilometers to South in a wider canal. With Gumbell law the expected intensity grows with time for occurrence, indicating a value for each given "return period", but the increasing speed grows with the "annual dispersion" of the Gumbell law, and some rare dangerous events may become really very possible in periods of many years. That can be proved with relatively simple models, e.g. with Extreme Law type I, and they could be made more precise or discussed. Such effects were used for superposition of actions on a structure for Model Codes, and may be combined with hydraulic effects, e.g. for bridges on rivers. These different Gumbell laws, or other extreme laws, with different dispersion may occur for marine actions of waves, earthquakes, tsunamis, and maybe for human perturbations, that could include industrial catastrophes, or civilization wars if considering historical periods.

  8. Development of pre-processing method for use of meteorological ensemble predictions as input to hydrological models: case study of the Huai River Basin, China

    NASA Astrophysics Data System (ADS)

    Song, W.; Xu, X.; Duan, Q.; van Andel, S. J.; Lobbrecht, A. H.; Solomatine, D. P.

    2012-04-01

    Hydrological models are run with precipitation and other meteorological data as input. With adequate spatial and temporal coverage, observed meteorological data is more reliable and more accurate for hydrological models, conventionally. However, when an early warning with several days ahead needs to be provided (for example for flood forecasts) in many cases Numerical Weather Prediction (NWP) has to be used. Hydro-meteorological forecasters and researchers aim to understand and minimize the forecast uncertainties. This research's focus is on testing existing methods and/or developing new methods to improve the integration between meteorological models and hydrological models. The output of the fixed version of the NCEP GFS meteorological ensemble prediction system for the Huai river basin is used in this research with the lead time of 1 to 15 days. There are a number of pre-processing methods to be tested, such as Quantile-to-Quantile correction methods, Analog methods, and Logistic regression. Apart from a single method, a multi-methods system could be developed as well - employing BMA, ANN or other model combinational algorithms. Furthermore, the processed ensemble meteorological data can be fed into hydrological models to generate ensemble discharge forecasts. Performance of this approach is tested by comparing its output with ensemble discharge forecasts on the basis of the raw meteorological ensemble forecast input.

  9. Advances in fatigue life prediction methodology for metallic materials

    NASA Technical Reports Server (NTRS)

    Newman, J. C., Jr.

    1992-01-01

    The capabilities of a plasticity-induced crack-closure model to predict small- and large-crack growth rates, and in some cases total fatigue life, for four aluminum alloys and three titanium alloys under constant-amplitude, variable-amplitude, and spectrum loading are described. Equations to calculate a cyclic-plastic-zone corrected effective stress-intensity factor range from a cyclic J-integral and crack-closure analysis of large cracks were reviewed. The effective stress-intensity factor range against crack growth rate relations were used in the closure model to predict small- and large-crack growth under variable-amplitude and spectrum loading. Using the closure model and microstructural features, a total fatigue life prediction method is demonstrated for three aluminum alloys under various load histories.

  10. Advances in the Assessment and Prediction of Interpersonal Violence

    ERIC Educational Resources Information Center

    Mills, Jeremy F.

    2005-01-01

    This article underscores the weakness of clinical judgment as a mechanism for prediction with examples from other areas in the psychological literature. Clinical judgment has as its Achilles' heel the reliance on a person to incorporate multiple pieces of information while overcoming human judgment errors - a feat insurmountable thus far. The…

  11. PREDICTIVE UNCERTAINTY IN HYDROLOGIC AND WATER QUALITY MODELING: APPROACHES, APPLICATION TO ENVIRONMENTAL MANAGEMENT, AND FUTURE CHALLENGES (PRESENTATION)

    EPA Science Inventory

    Extant process-based hydrologic and water quality models are indispensable to water resources planning and environmental management. However, models are only approximations of real systems and often calibrated with incomplete and uncertain data. Reliable estimates, or perhaps f...

  12. Application of spatially distributed coupled glacio-hydrological model to predict the effect of glacier recession on the flow of the Upper Bow River, Alberta, Canada

    NASA Astrophysics Data System (ADS)

    Naz, B. S.; Frans, C. D.; Clarke, G. K.; Nolin, A. W.; Lettenmaier, D. P.; Istanbulluoglu, E.; Burns, P. J.

    2011-12-01

    Several recent studies have suggested that observed decreases in summer flows in Canada's South Saskatchewan River are partly due to retreat of glaciers in the river's headwaters. Despite the risk posed by declining glaciers to water supply in the high mountain river systems, our ability to accurately predict runoff contribution from partially glacierized basins is limited. Modeling the effect of glacier changes on streamflow response in such basins is complicated due to limited availability of high resolution gridded meteorological data, lack of long term measurements of glaciological parameters and most importantly glacier dynamics are not linked to hydrological processes in many existing physically-based distributed hydrologic models. We investigate the effect of glacier recession on streamflow variations for the Upper Bow River basin, a tributary of the South Saskatchewan, near Lake Louise, Alberta, using the Distributed Hydrology Soil Vegetation Model (DHSVM) coupled with the spatially distributed glacier dynamics model. The coupled model is forced with the North American Regional Reanalysis (NARR) climate data for the period of 1979 - 2010 at a 3-hourly time step. The NARR data are adjusted for spatial variability in precipitation and temperature using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) monthly data at 2.5 arcmin resolution made available through the Climate Western North America (ClimateWNA) database (Wang et al. 2006). Using known subglacial bed topography information, a multidecade spin-up run of the stand alone glacier model is first conducted until the beginning of the simulation period for the coupled model to accurately predict ice thickness confirmed through comparison of modeled ice margins with observed glacier extent. The integrated model initialized with already estimated glacier thickness and ice extent is then run to predict glacier evolution, including spatial extent in combination with other hydrologic

  13. Use of oxygen-18 and deuterium to assess the hydrology of groundwater-lake systems: Chapter 3: Advances in chemistry

    USGS Publications Warehouse

    Krabbenhoft, David P.; Bowser, Carl J.; Kendall, Carol; Gat, Joel

    2009-01-01

    A thorough understanding of a lake's hydrology is essential for many lake studies. In some situations the interactions between groundwater systems and lakes are complex; in other cases the hydrology of a multilake system needs to be quantified. In such places, stable isotopes offer an alternative to the more traditional piezometer networks, which are costly to install and time-consuming to maintain. The stable-isotope mass-balance relations presented here can be used to estimate groundwater exchange rates for individual lakes and geographically clustered lakes. These relations also can be used to estimate other hydrological factors, such as average relative humidity. In places where the groundwater system is unstable (e.g., where flow reversals occur), natural solute tracers may provide a better alternative than stable isotopes for estimating rates of groundwater flow to and from lakes.

  14. Life prediction of advanced materials for gas turbine application

    SciTech Connect

    Zamrik, S.Y.; Ray, A.; Koss, D.A.

    1995-10-01

    Most of the studies on the low cycle fatigue life prediction have been reported under isothermal conditions where the deformation of the material is strain dependent. In the development of gas turbines, components such as blades and vanes are exposed to temperature variations in addition to strain cycling. As a result, the deformation process becomes temperature and strain dependent. Therefore, the life of the component becomes sensitive to temperature-strain cycling which produces a process known as {open_quotes}thermomechanical fatigue, or TMF{close_quotes}. The TMF fatigue failure phenomenon has been modeled using conventional fatigue life prediction methods, which are not sufficiently accurate to quantitatively establish an allowable design procedure. To add to the complexity of TMF life prediction, blade and vane substrates are normally coated with aluminide, overlay or thermal barrier type coatings (TBC) where the durability of the component is dominated by the coating/substrate constitutive response and by the fatigue behavior of the coating. A number of issues arise from TMF depending on the type of temperature/strain phase cycle: (1) time-dependent inelastic behavior can significantly affect the stress response. For example, creep relaxation during a tensile or compressive loading at elevated temperatures leads to a progressive increase in the mean stress level under cyclic loading. (2) the mismatch in elastic and thermal expansion properties between the coating and the substrate can lead to significant deviations in the coating stress levels due to changes in the elastic modulii. (3) the {open_quotes}dry{close_quotes} corrosion resistance coatings applied to the substrate may act as primary crack initiation sites. Crack initiation in the coating is a function of the coating composition, its mechanical properties, creep relaxation behavior, thermal strain range and the strain/temperature phase relationship.

  15. Finding diversity for building one-day ahead Hydrological Ensemble Prediction System based on artificial neural network stacks

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Anctil, Francois; Gagné, Christian; López, Karol

    2013-04-01

    In this study, we addressed the application of Artificial Neural Networks (ANN) in the context of Hydrological Ensemble Prediction Systems (HEPS). Such systems have become popular in the past years as a tool to include the forecast uncertainty in the decision making process. HEPS considers fundamentally the uncertainty cascade model [4] for uncertainty representation. Analogously, the machine learning community has proposed models of multiple classifier systems that take into account the variability in datasets, input space, model structures, and parametric configuration [3]. This approach is based primarily on the well-known "no free lunch theorem" [1]. Consequently, we propose a framework based on two separate but complementary topics: data stratification and input variable selection (IVS). Thus, we promote an ANN prediction stack in which each predictor is trained based on input spaces defined by the IVS application on different stratified sub-samples. All this, added to the inherent variability of classical ANN optimization, leads us to our ultimate goal: diversity in the prediction, defined as the complementarity of the individual predictors. The stratification application on the 12 basins used in this study, which originate from the second and third workshop of the MOPEX project [2], shows that the informativeness of the data is far more important than the quantity used for ANN training. Additionally, the input space variability leads to ANN stacks that outperform an ANN stack model trained with 100% of the available information but with a random selection of dataset used in the early stopping method (scenario R100P). The results show that from a deterministic view, the main advantage focuses on the efficient selection of the training information, which is an equally important concept for the calibration of conceptual hydrological models. On the other hand, the diversity achieved is reflected in a substantial improvement in the scores that define the

  16. Modelling Aerodynamically Generated Sound: Recent Advances in Rotor Noise Prediction

    NASA Technical Reports Server (NTRS)

    Brentner, Kenneth S.

    2000-01-01

    A great deal of progress has been made in the modeling of aerodynamically generated sound for rotors over the past decade. The Ffowcs Williams-Hawkings (FW-H ) equation has been the foundation for much of the development. Both subsonic and supersonic quadrupole noise formulations have been developed for the prediction of high-speed impulsive noise. In an effort to eliminate the need to compute the quadrupole contribution, the FW-H has also been utilized on permeable surfaces surrounding all physical noise sources. Comparison of the Kirchhoff formulation for moving surfaces with the FW-H equation have shown that the Kirchhoff formulation for moving surfaces can give erroneous results for aeroacoustic problems.

  17. Recent advances using rodent models for predicting human allergenicity

    SciTech Connect

    Knippels, Leon M.J. . E-mail: Knippels@voeding.tno.nl; Penninks, Andre H.

    2005-09-01

    The potential allergenicity of newly introduced proteins in genetically engineered foods has become an important safety evaluation issue. However, to evaluate the potential allergenicity and the potency of new proteins in our food, there are still no widely accepted and reliable test systems. The best-known allergy assessment proposal for foods derived from genetically engineered plants was the careful stepwise process presented in the so-called ILSI/IFBC decision tree. A revision of this decision tree strategy was proposed by a FAO/WHO expert consultation. As prediction of the sensitizing potential of the novel introduced protein based on animal testing was considered to be very important, animal models were introduced as one of the new test items, despite the fact that non of the currently studied models has been widely accepted and validated yet. In this paper, recent results are summarized of promising models developed in rat and mouse.

  18. Advanced electric field computation for RF sheaths prediction with TOPICA

    NASA Astrophysics Data System (ADS)

    Milanesio, Daniele; Maggiora, Riccardo

    2012-10-01

    The design of an Ion Cyclotron (IC) launcher is not only driven by its coupling properties, but also by its capability of maintaining low parallel electric fields in front of it, in order to provide good power transfer to plasma and to reduce the impurities production. However, due to the impossibility to verify the antenna performances before the starting of the operations, advanced numerical simulation tools are the only alternative to carry out a proper antenna design. With this in mind, it should be clear that the adoption of a code, such as TOPICA [1], able to precisely take into account a realistic antenna geometry and an accurate plasma description, is extremely important to achieve these goals. Because of the recently introduced features that allow to compute the electric field distribution everywhere inside the antenna enclosure and in the plasma column, the TOPICA code appears to be the only alternative to understand which elements may have a not negligible impact on the antenna design and then to suggest further optimizations in order to mitigate RF potentials. The present work documents the evaluation of the electric field map from actual antennas, like the Tore Supra Q5 and the JET A2 launchers, and the foreseen ITER IC antenna. [4pt] [1] D. Milanesio et al., Nucl. Fusion 49, 115019 (2009).

  19. Designing Hydroecologic - Geomorphic Monitoring Networks to Capture Heterogeneity and Predict the Influence of Climate Change on Hydrologic, Ecologic and Geomorphic Processes

    NASA Astrophysics Data System (ADS)

    Tennant, C. J.; Crosby, B. T.

    2010-12-01

    Regional differences in topography and climate in mountainous catchments cause heterogeneity in hydrologic and geomorphic processes and complicate the prediction of climate change impacts on anthropogenic and ecologic systems. To elucidate these complexities and provide measurements that strengthen our predictive power of the influence of climate change on hydrologic, ecologic and geomorphic processes we have designed and implemented a monitoring network that encompasses large elevation, climatic and orographic gradients in the Salmon River basin, central Idaho. Our methodology places strong influence on understanding and characterizing the range and distribution of physical elements across a landscape. Elevation is a characteristic of landscapes that can vary a great deal within a single drainage and exerts strong control on precipitation phase and magnitude, affecting hydroecologic and geomorphic processes. We use hypsometry to characterize elevation distributions and provide a novel method to assess the sensitivity of a landscape to rising snowlines. For example, hypsometry can be used to identify landscape features such as large plateaus or valleys that make up a significant percentage of a watershed’s total land area yet are confined within a small elevation range. These types of landscape elements that are close to freezing line elevations can be thought of as thresholds that will yield non-linear responses to rising snowlines. They will produce disproportionate affects on the percentage of a basin that transitions from snow, to rain domination. In addition, we have used hypsometry to identify sub-basins of the Salmon River that are contained entirely within distinct elevation zones. The low (400 - 1800 m), mid (1000 - 2200 m) and high (2200 - 3200 m) elevation ranges correspond to distinct precipitation regimes (liquid, mixed-phase, and solid phase respectively). From our hydrologic monitoring network we have learned that the frequency, duration and

  20. Investigation of advanced UQ for CRUD prediction with VIPRE.

    SciTech Connect

    Eldred, Michael Scott

    2011-09-01

    This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. It demonstrates the application of 'advanced UQ,' in particular dimension-adaptive p-refinement for polynomial chaos and stochastic collocation. The study calculates statistics for several quantities of interest that are indicators for the formation of CRUD (Chalk River unidentified deposit), which can lead to CIPS (CRUD induced power shift). Stochastic expansion methods are attractive methods for uncertainty quantification due to their fast convergence properties. For smooth functions (i.e., analytic, infinitely-differentiable) in L{sup 2} (i.e., possessing finite variance), exponential convergence rates can be obtained under order refinement for integrated statistical quantities of interest such as mean, variance, and probability. Two stochastic expansion methods are of interest: nonintrusive polynomial chaos expansion (PCE), which computes coefficients for a known basis of multivariate orthogonal polynomials, and stochastic collocation (SC), which forms multivariate interpolation polynomials for known coefficients. Within the DAKOTA project, recent research in stochastic expansion methods has focused on automated polynomial order refinement ('p-refinement') of expansions to support scalability to higher dimensional random input spaces [4, 3]. By preferentially refining only in the most important dimensions of the input space, the applicability of these methods can be extended from O(10{sup 0})-O(10{sup 1}) random variables to O(10{sup 2}) and beyond, depending on the degree of anisotropy (i.e., the extent to which randominput variables have differing degrees of influence on the statistical quantities of interest (QOIs)). Thus, the purpose of this study is to investigate the application of these adaptive stochastic expansion methods to the analysis of CRUD using the VIPRE simulation tools for two different plant models of differing random dimension, anisotropy, and

  1. Predicting foraging wading bird populations in Everglades National Park from seasonal hydrologic statistics under different management scenarios

    NASA Astrophysics Data System (ADS)

    Kwon, Hyun-Han; Lall, Upmanu; Engel, Vic

    2011-09-01

    The ability to map relationships between ecological outcomes and hydrologic conditions in the Everglades National Park (ENP) is a key building block for their restoration program, a primary goal of which is to improve conditions for wading birds. This paper presents a model linking wading bird foraging numbers to hydrologic conditions in the ENP. Seasonal hydrologic statistics derived from a single water level recorder are well correlated with water depths throughout most areas of the ENP, and are effective as predictors of wading bird numbers when using a nonlinear hierarchical Bayesian model to estimate the conditional distribution of bird populations. Model parameters are estimated using a Markov chain Monte Carlo (MCMC) procedure. Parameter and model uncertainty is assessed as a byproduct of the estimation process. Water depths at the beginning of the nesting season, the average dry season water level, and the numbers of reversals from the dry season recession are identified as significant predictors, consistent with the hydrologic conditions considered important in the production and concentration of prey organisms in this system. Long-term hydrologic records at the index location allow for a retrospective analysis (1952-2006) of foraging bird numbers showing low frequency oscillations in response to decadal fluctuations in hydroclimatic conditions. Simulations of water levels at the index location used in the Bayesian model under alternative water management scenarios allow the posterior probability distributions of the number of foraging birds to be compared, thus providing a mechanism for linking management schemes to seasonal rainfall forecasts.

  2. Advance prediction of hypotension at cesarean delivery under spinal anesthesia.

    PubMed

    Kinsella, S M; Norris, M C

    1996-01-01

    Cardiovascular responses to supine inferior vena cava compression might predict hypotension risk during elective cesarean delivery using spinal anesthesia. In this pilot study we investigated 27 women before operation by taking blood pressure and heart rate measurements for 5 min in the left lateral position, 5 min supine, and then performed one further reading in the left lateral position and one sitting. Anesthesia with hyperbaric bupivacaine was rigorously standardised. A pre-operative 'supine stress test', combining an increase in maternal heart rate of greater than 10 beats/min or leg flexion movements while supine, was analysed. A positive supine stress test (SST) was 4.1 times more frequent in those with severe systolic hypotension below 70% of baseline (12 out of 16 women) than in those without (2 out of 11 women), with a sensitivity of 75% (95% C.I. 48% to 93%) and specificity of 82% (95% C.I. 48% to 98%). A positive test was associated with twice as much vasopressor use as a negative test (30.7 +/-/14.5 mg versus 13.5 +/-/ 9.9 mg; P = 0.0014). Unlike the SST, cardiovascular responses to the change from recumbent to sitting (tilt test) were not useful as a predictor of hypotension. PMID:15321375

  3. Incorporating Satellite Remote Sensing Data into Hydrologic Models: Towards Improved Performance in Modeling the Past and Reduced Uncertainty in Predicting the Future

    NASA Astrophysics Data System (ADS)

    Parr, D.; Wang, G.

    2014-12-01

    In many regions of the worlds, studies of past hydrological variability have to rely on hydrological models either because river gauge measurement is not available or because measurements do not reflect the natural flow due to water diversion or reservoir regulation. However, results from these studies are subject to major uncertainty related to the challenges in quantifying vegetation conditions and evapotranspiration, both of which are important for surface water and energy budgets. This study incorporates satellite remote sensing data for ET and vegetation into the VIC model to improve the model performance in simulating the surface water budget, hydrological seasonality, and timing of hydrological extremes. Using the Connecticut River Basin as an example, and driven with the NASA NLDAS-2 meteorological forcing data, the VIC model has been modified to read in LAI and ET data derived from MODIS among others. The MODIS LAI data provides VIC with the inter-annually varying seasonal cycle of vegetation, and the MODIS ET data replaces the model simulated ET. The data-enhanced model performs significantly better in simulating river discharge, its magnitude, seasonality, timing, soil moisture and its temporal variation. Incorporation of the ET data led to an increase of stream flow correlations between model and observations on the daily and biweekly temporal scales, and the seasonality is better represented on a monthly scale with particular magnitude improvements during the summer when ET is greatest. Incorporation of the LAI data led to improved simulation of inter-annual variability. This joint application of remote sensing and modeling helps quantify the extent to which remote sensing data improves model performance, facilitates a more accurate understanding and attribution of past hydrological variability/changes, and helps characterize the range of model-related uncertainties in future predictions.

  4. Toward high-resolution flash flood prediction in large urban areas - Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling

    NASA Astrophysics Data System (ADS)

    Rafieeinasab, Arezoo; Norouzi, Amir; Kim, Sunghee; Habibi, Hamideh; Nazari, Behzad; Seo, Dong-Jun; Lee, Haksu; Cosgrove, Brian; Cui, Zhengtao

    2015-12-01

    Urban flash flooding is a serious problem in large, highly populated areas such as the Dallas-Fort Worth Metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to providing location-specific early warnings and cost-effective emergency management in such areas. Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases. In reality, however, due to the errors in the precipitation input, and in the structures, parameters and states of the models, there are practical limits to the model resolution. In this work, we assess the sensitivity of streamflow simulation in urban catchments to the spatiotemporal resolution of precipitation input and hydrologic modeling to identify the resolution at which the simulation errors may be at minimum given the quality of the precipitation input and hydrologic models used, and the response time of the catchment. The hydrologic modeling system used in this work is the National Weather Service (NWS) Hydrology Laboratory's Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250 m to 2 km and from 1 min to 1 h applied over the Cities of Fort Worth, Arlington and Grand Prairie in DFW. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars. For comparison, the NWS Multisensor Precipitation Estimator (MPE) product, which is available at a 4-km 1-h resolution, was also used. The streamflow simulation results are evaluated for 5 urban catchments ranging in size from 3.4 to 54.6 km2 and from about 45 min to 3 h in time-to-peak in the Cities of Fort Worth, Arlington and Grand Prairie. The streamflow observations used in evaluation were obtained from water level measurements via rating

  5. A computational framework to advance hydrometeorological prediction capabilities in cold regions

    NASA Astrophysics Data System (ADS)

    Clark, M. P.; Kavetski, D.; Slater, A. G.; Lundquist, J. D.; Wood, A. W.; Gochis, D. J.; Gutmann, E. D.; Rasmussen, R.

    2012-12-01

    Many different modeling groups recognize the need for new computational frameworks for use as both (i) a model development tool to evaluate competing process representations; and (ii) a predictive tool to reliably represent model uncertainty. Here we describe a computational framework to explore different approaches for modeling the hydrology and thermodynamics of snow and partially frozen soils. The framework has two main features: it has a "numerically agile" structural core to support evaluating the impact of different numerical approximations (e.g., vertical discretization, linearizations, etc.), and it has the modularity to support experimenting with different constitutive functions and boundary conditions. The broad flexibility of the framework facilitates constructing multiple equally plausible model realizations - these realizations can be used either as ensembles to represent model uncertainty, or examined in a systematic way to isolate the impact of individual model components on model predictions and hence facilitate a controlled approach to hypothesis testing. Application of the framework in different snow environments emphasizes the impact of (and interactions among) different modeling decisions. The approaches used to parameterize turbulent heat fluxes, parameters controlling the storage of liquid water in the snowpack, and the lower boundary conditions for hydrology were especially important in the case studies examined. More generally, results show that the impacts of differences in model structure are often overwhelmed by uncertainty in a-priori estimates of model parameters, and suggest that careful specification of probability distributions of model parameters can be used to represent model uncertainty.

  6. A New Data Assimilation Framework for Enhancing Hydrologic Predictions using Remotely-Sensed Surface Soil Moisture Retrievals

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A large number of recent studies have focused on improving rainfall/runoff and stream flow modeling via the assimilation of remotely sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from a state-estimation perspective – in whic...

  7. Multimodel hydrologic ensemble predictions of peak flows: lessons learned from the real-time experiment in the upper Nysa Klodzka basin (SW Poland)

    NASA Astrophysics Data System (ADS)

    Niedzielski, Tomasz; Mizinski, Bartlomiej

    2015-04-01

    The novel system for issuing the real-time warnings against hydrologic hazards, known as HydroProg (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland), has been implemented in the upper Nysa Klodzka basin (SW Poland). The system itself works like a bridge between automatic hydrometeorological observational networks and numerous hydrologic models. Its main objective is to automatically produce and publish flood warnings on a basis of prognoses of river stages calculated from dissimilar models and - most importantly - their multimodel ensembles which are computed in real time within HydroProg. The implementation in question for the upper Nysa Klodzka basin is abbreviated as HydroProg-Klodzko, and is feasible due to the partnership with Klodzko County which maintains the Local System for Flood Monitoring (Lokalny System Oslony Przeciwpowodziowej - LSOP). The HydroProg-Klodzko prototype is continuously, i.e. with 15-minute update, calculating multimodel hydrologic ensemble predictions and publishing them along with prognoses corresponding to individual ensemble members (www.klodzko.hydroprog.uni.wroc.pl). The real-time HydroProg-Klodzko experiment provided us with a valuable database of predictions as well as their errors and performance characteristics. At present, six hydrologic models participate in the experiment, however two of them (multi- and univariate autoregressive time series models) work uninterruptedly since the launch of the system in August 2013. The present study focuses on the detailed characterization of the real-time performance of the two models in predicting a few significant peak flows that occurred over the entire year of the experiment. In particular, we show how the two models can be weighted to produce skilful multimodel ensemble prognoses of river stages during peak flows. We identify phases of a peak flow in which, in order to improve the predictive skills, one should switch between individual models and

  8. Parameterization of Natural Depressions in Distributed Hydrologic Models: Implications for Scaling up Predictions of Sediment and Nutrient Yields in Ungauged Agricultural Watersheds

    NASA Astrophysics Data System (ADS)

    Chien, H.; Mackay, S.; Cabot, P. E.; Karthikeyan, K.

    2005-12-01

    Digital Elevation Models (DEMs) are widely used in distributed hydrologic modeling. In general, interior depressions within catchments are viewed as errors in the DEM, even though they are hydrologically significant features. Natural depressions in catchments are capable of trapping surface runoff and associated sediment, but they are difficult to identify and represent, especially in ungauged basins. We examined the errors associated with the removal of such depressions on predictions from hydrologic models, Soil and Water Assessment Tool (SWAT) and Agricultural Policy/Environmental eXtender (APEX). Automated water and sediment samplers were installed in the outlets of three natural depressions in a small catchment in the North Fork of Pheasant Branch watershed in Dane County, Wisconsin, to collect surface runoff and sediment yields for the period 2003-2004. The data showed that when daily precipitation is over 26 mm, surface runoff with suspended sediment overtops the depressions. SWAT and APEX were calibrated to this data to examine the influence of nested depressions on sediment yields. The hypothesis addressed in this study is: sediment transport parameters can be used as proxies for the functioning of surface depression and to obtain the correct sediment response. The alternative is to explicitly prescribe depressions as reservoirs with more geometric details of depressions if the hypothesis failed. Initial model results showed that the adjustment of sediment transport parameters mimics the response of the depressions and significantly reduces sediment yields. Implications of a simple proxy of sediment deposition for scaling to larger, ungauged basins will be discussed.

  9. Predicting the type, location and magnitude of geomorphic responses to dam removal: Role of hydrologic and geomorphic constraints

    NASA Astrophysics Data System (ADS)

    Gartner, John D.; Magilligan, Francis J.; Renshaw, Carl E.

    2015-12-01

    thousands of dams likely to be considered for removal or repair in the coming decades, this study helps to advance predictions of the geomorphic response to dam removal and contributes to a broader understanding of the variability in both style and timing of fluvial responses to disturbances.

  10. Evaluation and development of hydrological parameterisations for the atmosphere, ocean and land surface coupled model developed by the UK Environmental Prediction (UKEP) Prototype project

    NASA Astrophysics Data System (ADS)

    Martinez-de la Torre, Alberto; Blyth, Eleanor; Ashton, Heather; Lewis, Huw

    2016-04-01

    The UKEP project brings together atmosphere, ocean and land surface models and scientist to build a coupled prediction system for the UK at 1.5 km scale. JULES (Joint UK Land-Environment Simulator) is the land surface model that generates runoff and simulates soil hydrology within the coupled prediction system. Here we present an evaluation of JULES performance at producing river flow for 13 selected catchments in Great Britain, where we use daily river flow observations at the catchment outlets. The evaluation is based on the Nush-Sutcliffe metric and bias. Results suggest that the inclusion of a new linear topographic slope dependency in the S0 parameter of the PDM (Probability Distributed Model, scheme that generates saturation excess runoff at the land surface when the soil water storage reaches S0), improves results for all catchments, constraining the surface runoff production for flatter catchments during rainy episodes. The new hydrological configuration developed offline using the JULES model has been implemented in the coupled prediction system for an intense winter storm case study. We found significant changes in accumulated runoff and total column soil moisture, and results consistent with the offline experiments with an increase in surface runoff on the high slopes of Scotland.

  11. Time Changes of the European Gravity Field from GRACE: A Comparison with Ground Measurements from Superconducting Gravimeters and with Hydrology Model Predictions

    NASA Technical Reports Server (NTRS)

    Hinderer, J.; Lemoine, Frank G.; Crossley, D.; Boy, J.-P.

    2004-01-01

    We investigate the time-variable gravity changes in Europe retrieved from the initial GRACE monthly solutions spanning a 18 month duration from April 2002 to October 2003. Gravity anomaly maps are retrieved in Central Europe from the monthly satellite solutions we compare the fields according to various truncation levels (typically between degree 10 and 20) of the initial fields (expressed in spherical harmonics to degree 120). For these different degrees, an empirical orthogonal function (EOF) decomposition of the time-variable gravity field leads us to its main spatial and temporal characteristics. We show that the dominant signal is found to be annual with an amplitude and a phase both in agreement with predictions in Europe modeled using snow and soil-moisture variations from recent hydrology models. We compare these GRACE gravity field changes to surface gravity observations from 6 superconducting gravimeters of the GGP (Global Geodynamics Project) European sub-network, with a special attention to loading corrections. Initial results suggest that all 3 data sets (GRACE, hydrology and GGP) are responding to annual changes in near-surface water in Europe of a few microGal (at length scales of approx.1000 km) that show a high value in winter and a summer minimum. We also point out that the GRACE gravity field evolution seems to indicate that there is a trend in gravity between summer 2002 and summer 2003 which can be related to the 2003 heatwave in Europe and its hydrological consequences (drought). Despite the limited time span of our analysis and the uncertainties in retrieving a regional solution from the network of gravimeters, the calibration and validation aspects of the GRACE data processing based on the annual hydrology cycle in Europe are in progress.

  12. Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation

    PubMed Central

    Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

    2008-01-01

    This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems.

  13. Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

    NASA Technical Reports Server (NTRS)

    Yong, Bin; Hong, Yang; Ren, Li-Liang; Gourley, Jonathan; Huffman, George J.; Chen, Xi; Wang, Wen; Khan, Sadiq I.

    2013-01-01

    The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.

  14. Global-Scale Hydrology: Simple Characterization of Complex Simulation

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.

    1999-01-01

    Atmospheric general circulation models (AGCMS) are unique and valuable tools for the analysis of large-scale hydrology. AGCM simulations of climate provide tremendous amounts of hydrological data with a spatial and temporal coverage unmatched by observation systems. To the extent that the AGCM behaves realistically, these data can shed light on the nature of the real world's hydrological cycle. In the first part of the seminar, I will describe the hydrological cycle in a typical AGCM, with some emphasis on the validation of simulated precipitation against observations. The second part of the seminar will focus on a key goal in large-scale hydrology studies, namely the identification of simple, overarching controls on hydrological behavior hidden amidst the tremendous amounts of data produced by the highly complex AGCM parameterizations. In particular, I will show that a simple 50-year-old climatological relation (and a recent extension we made to it) successfully predicts, to first order, both the annual mean and the interannual variability of simulated evaporation and runoff fluxes. The seminar will conclude with an example of a practical application of global hydrology studies. The accurate prediction of weather statistics several months in advance would have tremendous societal benefits, and conventional wisdom today points at the use of coupled ocean-atmosphere-land models for such seasonal-to-interannual prediction. Understanding the hydrological cycle in AGCMs is critical to establishing the potential for such prediction. Our own studies show, among other things, that soil moisture retention can lead to significant precipitation predictability in many midlatitude and tropical regions.

  15. Assessment of NEXRAD and Rain Gauge Precipitation Data for Hydrological Response Predictions in the St Joseph River Watershed, USA

    NASA Astrophysics Data System (ADS)

    Heathman, G.; Larose, M.; Huang, C.

    2009-04-01

    Precipitation is a major driving force variable behind all hydrologic processes needed for watershed modeling studies. The use of point-scale rain gauge data in watershed hydrologic models may not effectively capture the spatial distribution of rainfall; thereby, directly affecting the water balance and introducing large uncertainty in the modeling outcome. Rain gauges typically measure the depth of precipitation within a 100 cm2 sampling area (i.e., tipping bucket). Although they usually provide high quality data, a dense rain gauge network must be established to capture the spatial variability of precipitation in an area. Spatially distributed precipitation, such as radar precipitation products from the Next Generation Weather Radar (NEXRAD) of the U.S. National Weather Service, should provide better estimates of the rainfall distribution over large watershed areas. However, NEXRAD estimates may introduce errors due to drop size distributions of rainfall and properties inherent in the radar measurement system. Consequently, there is a need to evaluate NEXRAD Stage III precipitation data against rain gauge precipitation data that are not included in the processing algorithm, as they become available before being used in hydrologic studies. Thus, the objective of this study was to examine the possible sources of error in the Stage III product through radar-gauge intercomparisons using a 3-yr record (2005-2007) of precipitation data from the Agricultural Research Service, National Soil Erosion Laboratory in northeastern Indiana, USA. The results show that the Stage III system estimated an average of 1035.5 mm of precipitation over the rain gauge network area while rain gauges recorded an average of 955.1 mm. The differences in total precipitation depth and percent bias between the Stage III and rain gauge data were 80.4 mm and 8.4 percent, respectively. Stage III overestimation was observed at four out the five rain gauges. Modeling results of watershed hydrologic

  16. Prediction models for platinum-based chemotherapy response and toxicity in advanced NSCLC patients.

    PubMed

    Yin, Ji-Ye; Li, Xi; Li, Xiang-Ping; Xiao, Ling; Zheng, Wei; Chen, Juan; Mao, Chen-Xue; Fang, Chao; Cui, Jia-Jia; Guo, Cheng-Xian; Zhang, Wei; Gao, Yang; Zhang, Chun-Fang; Chen, Zi-Hua; Zhou, Hui; Zhou, Hong-Hao; Liu, Zhao-Qian

    2016-07-10

    In this study, we aimed to establish a platinum-based chemotherapy response and toxicity prediction model in advanced non-small cell lung cancer (NSCLC) patients. 416 single nucleotide polymorphisms (SNPs) in 185 genes were genotyped, and their association with drug response and toxicity were estimated using logistic regression. Nine data mining techniques were employed to establish the prediction model; the sensitivity, specificity, overall accuracy and receiver operating characteristic (ROC) curve were used to assess the models' performance. Finally, selected models were validated in an independent cohort. The models established by naïve Bayesian algorithm had the best performance. The response prediction model achieved a sensitivity of 0.90 and a specificity of 0.47 with the ROC area under curve (AUC) of 0.80. The overall toxicity prediction model achieved a sensitivity of 0.86 and a specificity of 0.46 with the ROC AUC of 0.73. The hematological toxicity prediction model achieved a sensitivity of 0.89 and a specificity of 0.39 with the ROC AUC of 0.76. The gastrointestinal toxicity prediction model achieved a sensitivity of 0.93 and a specificity of 0.35 with the ROC AUC of 0.80. In conclusion, we provided platinum-based chemotherapy response and toxicity prediction models for advanced NSCLC patients. PMID:27126360

  17. Using discharge data to reduce structural deficits in a hydrological model with a Bayesian inference approach and the implications for the prediction of critical source areas

    NASA Astrophysics Data System (ADS)

    Frey, M. P.; Stamm, C.; Schneider, M. K.; Reichert, P.

    2011-12-01

    A distributed hydrological model was used to simulate the distribution of fast runoff formation as a proxy for critical source areas for herbicide pollution in a small agricultural catchment in Switzerland. We tested to what degree predictions based on prior knowledge without local measurements could be improved upon relying on observed discharge. This learning process consisted of five steps: For the prior prediction (step 1), knowledge of the model parameters was coarse and predictions were fairly uncertain. In the second step, discharge data were used to update the prior parameter distribution. Effects of uncertainty in input data and model structure were accounted for by an autoregressive error model. This step decreased the width of the marginal distributions of parameters describing the lower boundary (percolation rates) but hardly affected soil hydraulic parameters. Residual analysis (step 3) revealed model structure deficits. We modified the model, and in the subsequent Bayesian updating (step 4) the widths of the posterior marginal distributions were reduced for most parameters compared to those of the prior. This incremental procedure led to a strong reduction in the uncertainty of the spatial prediction. Thus, despite only using spatially integrated data (discharge), the spatially distributed effect of the improved model structure can be expected to improve the spatially distributed predictions also. The fifth step consisted of a test with independent spatial data on herbicide losses and revealed ambiguous results. The comparison depended critically on the ratio of event to preevent water that was discharged. This ratio cannot be estimated from hydrological data only. The results demonstrate that the value of local data is strongly dependent on a correct model structure. An iterative procedure of Bayesian updating, model testing, and model modification is suggested.

  18. Positive impact of the new 5-layer soil-hydrology scheme on seasonal prediction skill of 2-meter air temperatures over Europe

    NASA Astrophysics Data System (ADS)

    Bunzel, Felix; Müller, Wolfgang; Stacke, Tobias; Hagemann, Stefan; Dobrynin, Mikhail; Baehr, Johanna; Fröhlich, Kristina

    2016-04-01

    Recent studies show that the initialization of soil moisture has the potential to improve the skill of seasonal predictions with coupled climate models. Particularly, soil-moisture memory in the root zone is found to affect the predictability of surface state variables. However, in order to simulate the connection between root-zone soil-moisture and the near-surface atmospheric state realistically, the soil-hydrology scheme implemented in a coupled climate model requires a certain level of complexity. In this study, we first compare the quality of soil-moisture simulation in full-field assimilation experiments performed with the Max Planck Institute Earth System Model (MPI-ESM) in two different setups, one using the old bucket-type soil scheme and one using the new 5-layer soil-hydrology scheme. We find soil moisture to be more realistically simulated when MPI-ESM is used with the new 5-layer soil scheme. In a second step, from each of the two assimilation experiments a set of seasonal hindcast simulations is started. Each hindcast set consists of 10-member ensembles initialized on 1 May and 1 November each year within 1981-2012 with a hindcast length of 6 months each. We find the new 5-layer soil-hydrology scheme to improve the hindcast skill of both summer and winter 2-meter air temperatures over Europe compared to the old bucket-type soil scheme. In order to find possible sources for the improvement, land-atmosphere coupling is analyzed in the two hindcast sets, and a potential link to the atmospheric blocking frequency is investigated.

  19. Classification of simulated and actual NOAA-6 AVHRR data for hydrologic land-surface feature definition. [Advanced Very High Resolution Radiometer

    NASA Technical Reports Server (NTRS)

    Ormsby, J. P.

    1982-01-01

    An examination of the possibilities of using Landsat data to simulate NOAA-6 Advanced Very High Resolution Radiometer (AVHRR) data on two channels, as well as using actual NOAA-6 imagery, for large-scale hydrological studies is presented. A running average was obtained of 18 consecutive pixels of 1 km resolution taken by the Landsat scanners were scaled up to 8-bit data and investigated for different gray levels. AVHRR data comprising five channels of 10-bit, band-interleaved information covering 10 deg latitude were analyzed and a suitable pixel grid was chosen for comparison with the Landsat data in a supervised classification format, an unsupervised mode, and with ground truth. Landcover delineation was explored by removing snow, water, and cloud features from the cluster analysis, and resulted in less than 10% difference. Low resolution large-scale data was determined useful for characterizing some landcover features if weekly and/or monthly updates are maintained.

  20. The Potential Response of Aquatic Biodiversity in the Midwestern United States to Predicted Changes in Climate Based On Output From Landscape Scale Hydrologic Models

    NASA Astrophysics Data System (ADS)

    Knouft, J.; Chien, H.

    2012-12-01

    North America contains the highest diversity of temperate aquatic species on Earth. However, aquatic ecosystems, particularly rivers and streams, are experiencing a variety of anthropogenic impacts, including the ongoing effects of changes in climate. Unfortunately, our understanding of the potential responses of aquatic taxa to variation in climate is limited, particularly across broad geographic regions. At the landscape scale, the interactions of climate and landuse/landcover can have significant impacts on the hydrologic characteristics of aquatic ecosystems, which should have a direct influence on the distribution and persistence of aquatic species. Consequently, the ability to predict spatial variation in streamflow at landscape scales is essential to understanding the potential impacts of changes in climate on these ecosystems in the coming century. The goal of this study is to assess the potential impacts of climate change on streamflow in watersheds located in the Midwestern United States, primarily in Illinois. In-stream hydrologic data are then integrated with species distribution data (fishes, crayfishes, and mussels) and an ecological niche modeling algorithm to predict the potential impact of changes in streamflow on species distributions. A distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), was calibrated and validated using a multi-gauge landscape-scale approach. The potential impacts of climate changes on water resources were assessed through the validated SWAT model, which includes weather data predictions from a variety of global climate models (GCM). Predictions based on future climate scenarios generally indicate that total annual streamflow will decrease in the future compared with recent streamflow (1991-1999). Seasonal streamflow patterns in the future are predicted to change in terms of increased streamflow in the winter but decreased streamflow in the summer, yet intra-annual variability in streamflow will tend to

  1. Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery.

    PubMed

    Shu, Kai-Hsiang; Wang, Chih-Hsien; Wu, Che-Hsiung; Huang, Tao-Min; Wu, Pei-Chen; Lai, Chien-Heng; Tseng, Li-Jung; Tsai, Pi-Ru; Connolly, Rory; Wu, Vin-Cent

    2016-01-01

    Urinary biomarkers augment the diagnosis of acute kidney injury (AKI), with AKI after cardiovascular surgeries being a prototype of prognosis scenario. Glutathione S-transferases (GST) were evaluated as biomarkers of AKI. Urine samples were collected in 141 cardiovascular surgical patients and analyzed for urinary alpha-(α-) and pi-(π-) GSTs. The outcomes of advanced AKI (KDIGO stage 2, 3) and all-cause in-patient mortality, as composite outcome, were recorded. Areas under the receiver operator characteristic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes. Thirty-eight (26.9%) patients had AKI, while 12 (8.5%) were with advanced AKI. Urinary π-GST differentiated patients with/without advanced AKI or composite outcome after surgery (p < 0.05 by generalized estimating equation). Urinary π-GST predicted advanced AKI at 3 hrs post-surgery (p = 0.033) and composite outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783. Using GAM, the cutoff value of 14.7 μg/L for π-GST showed the best performance to predict composite outcome. The addition of π-GST to the SOFA score improved risk stratification (total net reclassification index = 0.47). Thus, urinary π-GST levels predict advanced AKI or hospital mortality after cardiovascular surgery and improve in SOFA outcome assessment specific to AKI. PMID:27527370

  2. Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery

    PubMed Central

    Shu, Kai-Hsiang; Wang, Chih-Hsien; Wu, Che-Hsiung; Huang, Tao-Min; Wu, Pei-Chen; Lai, Chien-Heng; Tseng, Li-Jung; Tsai, Pi-Ru; Connolly, Rory; Wu, Vin-Cent

    2016-01-01

    Urinary biomarkers augment the diagnosis of acute kidney injury (AKI), with AKI after cardiovascular surgeries being a prototype of prognosis scenario. Glutathione S-transferases (GST) were evaluated as biomarkers of AKI. Urine samples were collected in 141 cardiovascular surgical patients and analyzed for urinary alpha-(α-) and pi-(π-) GSTs. The outcomes of advanced AKI (KDIGO stage 2, 3) and all-cause in-patient mortality, as composite outcome, were recorded. Areas under the receiver operator characteristic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes. Thirty-eight (26.9%) patients had AKI, while 12 (8.5%) were with advanced AKI. Urinary π-GST differentiated patients with/without advanced AKI or composite outcome after surgery (p < 0.05 by generalized estimating equation). Urinary π-GST predicted advanced AKI at 3 hrs post-surgery (p = 0.033) and composite outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783. Using GAM, the cutoff value of 14.7 μg/L for π-GST showed the best performance to predict composite outcome. The addition of π-GST to the SOFA score improved risk stratification (total net reclassification index = 0.47). Thus, urinary π-GST levels predict advanced AKI or hospital mortality after cardiovascular surgery and improve in SOFA outcome assessment specific to AKI. PMID:27527370

  3. The development of a sub-daily gridded rainfall product to improve hydrological predictions in Great Britain

    NASA Astrophysics Data System (ADS)

    Quinn, Niall; Freer, Jim; Coxon, Gemma; O'Loughlin, Fiachra; Woods, Ross; Liguori, Sara

    2015-04-01

    In Great Britain and many other regions of the world, flooding resulting from short duration, high intensity rainfall events can lead to significant economic losses and fatalities. At present, such extreme events are often poorly evaluated using hydrological models due, in part, to their rarity and relatively short duration and a lack of appropriate data. Such storm characteristics are not well represented by daily rainfall records currently available using volumetric gauges and/or derived gridded products. This research aims to address this important data gap by developing a sub-daily gridded precipitation product for Great Britain. Our focus is to better understand these storm events and some of the challenges and uncertainties in quantifying such data across catchment scales. Our goal is to both improve such rainfall characterisation and derive an input to drive hydrological model simulations. Our methodology involves the collation, error checking, and spatial interpolation of approximately 2000 rain gauges located across Great Britain, provided by the Scottish Environment Protection Agency (SEPA) and the Environment Agency (EA). Error checking was conducted over the entirety of the TBR data available, utilising a two stage approach. First, rain gauge data at each site were examined independently, with data exceeding reasonable thresholds marked as suspect. Second, potentially erroneous data were marked using a neighbourhood analysis approach whereby measurements at a given gauge were deemed suspect if they did not fall within defined bounds of measurements at neighbouring gauges. A total of eight error checks were conducted. To provide the user with the greatest flexibility possible, the error markers associated with each check have been recorded at every site. This approach aims to enable the user to choose which checks they deem most suitable for a particular application. The quality assured TBR dataset was then spatially interpolated to produce a national

  4. It's the parameters, stupid! Moving beyond multi-model and multi-physics approaches to characterize and reduce predictive uncertainty in process-based hydrological models

    NASA Astrophysics Data System (ADS)

    Clark, Martyn; Samaniego, Luis; Freer, Jim

    2014-05-01

    Multi-model and multi-physics approaches are a popular tool in environmental modelling, with many studies focusing on optimally combining output from multiple model simulations to reduce predictive errors and better characterize predictive uncertainty. However, a careful and systematic analysis of different hydrological models reveals that individual models are simply small permutations of a master modeling template, and inter-model differences are overwhelmed by uncertainty in the choice of the parameter values in the model equations. Furthermore, inter-model differences do not explicitly represent the uncertainty in modeling a given process, leading to many situations where different models provide the wrong results for the same reasons. In other cases, the available morphological data does not support the very fine spatial discretization of the landscape that typifies many modern applications of process-based models. To make the uncertainty characterization problem worse, the uncertain parameter values in process-based models are often fixed (hard-coded), and the models lack the agility necessary to represent the tremendous heterogeneity in natural systems. This presentation summarizes results from a systematic analysis of uncertainty in process-based hydrological models, where we explicitly analyze the myriad of subjective decisions made throughout both the model development and parameter estimation process. Results show that much of the uncertainty is aleatory in nature - given a "complete" representation of dominant hydrologic processes, uncertainty in process parameterizations can be represented using an ensemble of model parameters. Epistemic uncertainty associated with process interactions and scaling behavior is still important, and these uncertainties can be represented using an ensemble of different spatial configurations. Finally, uncertainty in forcing data can be represented using ensemble methods for spatial meteorological analysis. Our systematic

  5. Integrating climate science, glaciology and hydrology to predict future run-off at the Greenland ice sheet margin: A case study from Ilulissat, West Greenland.

    NASA Astrophysics Data System (ADS)

    Mottram, R. H.; Ahlstrøm, A. P.; Nielsen, C.; Reeh, N.; Stendel, M.; Andersen, S. Bech

    2009-04-01

    Predicting future hydrological regimes with regard to climate change is an increasingly important task for hydrologists. In polar regions the task is more difficult due to the lack of datasets and long term monitoring as well as logistical difficulties in remote and inaccessible basins. Here, we demonstrate a case study predicting the future run-off in a difficult to model hydrological basin by integrating a range of data, methods and numerical models. A study, evaluating the future conditions in the Pakitsup Akuliarusersua basin near Ilulissat, West Greenland, was initiated to determine the viability of a small hydropower scheme based around two lakes adjacent to the ice-sheet margin. This basin is mainly supplied by meltwater from the ice-sheet margin and the position of the ice sheet relative to the lakes makes them sensitive to changes in drainage pathways. We combined glaciological and hydrological models with data from climate models in order to resolve these issues. An ice dynamic model (Reeh, 1988), incorporating new digital terrain models for the ice sheet surface and basal topographies (Mottram and other, 2009), was driven by climate data from a combined global/regional climate model (HIRHAM4) for the period 1950-2080 (Stendel and others, 2007). The climate data was downscaled to catchment scale and corrected using observational data from the local area. The corrected HIRHAM4 output was used as input to a temperature-index mass-balance model (Reeh, 1991) and used to force the ice-dynamic model in order to predict the future ice sheet geometry and to drive meltwater production at the ice sheet surface. These ice sheet geometries were used to predict the size of the ice-sheet part of the hydrological basin for a range of different levels of ice sheet basal water pressure every 5 years from present day to 2080. Thus, the present analysis takes into account global and regional climate change, ice dynamical response and changes in the internal drainage system

  6. Effects of Modeling Variable Source Area Hydrology on Flow and Phosphorous Transport Predicted by a SWAT model for the Cannonsville Watershed

    NASA Astrophysics Data System (ADS)

    Woodbury, J.; Shoemaker, C. A.; Cowan, D.; Easton, Z. M.

    2009-12-01

    Variable source area (VSA) is the concept that runoff generating locations vary in both time and space, depending on the time of year, rainfall, temperature and topography. VSAs are driven by saturation excess overland flow, which occurs when a soil is saturated and any additional rainfall results in runoff. Since many water quality models, such as SWAT, use some form of the curve number equation to predict storm runoff, these models may fail to accurately describe the effects of variable source areas (VSAs). VSAs are mostly important in rural, humid areas, such as upstate New York. In this paper we will describe SWAT-VSA, which incorporates VSA principles into SWAT2005 with the use of wetness classes. We compare the model results to a SWAT2005 model for the same watershed. In both cases the models are used to estimate a time series of flow, sediment and phosphorous based on data from the 1200 km2 Cannonsville watershed. The SWAT model is a physically based, continuous time watershed model developed by the USDA. The model simulates long term runoff and nutrient losses in rural, agriculturally dominated watersheds. The SWAT-VSA model distributes overland flow in ways that are consistent with variable source area hydrology, while the SWAT2005 version does not. These two models are compared by calibrating each for flow, phosphorous and sediment against measured data. The models are then compared using the r2 and the percent difference between the simulated and measured values for the three outputs. After calibrating both of the models, flow, phosphorus and sediment are predicted similarly well by each model. The advantage of using VSA hydrology in a watershed model becomes apparent when looking at nutrient management techniques. With VSA hydrology, planners, land use managers and farmers can better predict where the most of the nutrient load originates from and plan accordingly. Correctly predicting where runoff is generated from has major implications for nutrient

  7. History of forest hydrology

    NASA Astrophysics Data System (ADS)

    McCulloch, James S. G.; Robinson, Mark

    1993-10-01

    Hydrology as a science and a technology is examined, as are some of the myths on the role of forests in hydrology and water resources. The history of catchment area research is traced, in Europe, in the USA and in East Africa, with particular reference to forest hydrology and, in the earlier years, to water quantity rather than water quality. The importance of associating physical process studies with hydrological systems' investigations, to enhance understanding of why particular catchments behave as they do, is stressed. Recent advances in hydrochemistry have been exploited to elucidate water flow paths within experimental catchments. Stimulated by requirements for research into acidification of surface waters, research catchments have proved to be valuable outdoor laboratories from which a much improved understanding of the flow processes has been achieved. Conflicting claims about the impacts of forestry are described and discussed.

  8. The Experimental Hydrology Wiki

    NASA Astrophysics Data System (ADS)

    Blume, Theresa; van Meerveld, Ilja; Graeff, Thomas

    2013-04-01

    The "Experimental Hydrology Wiki" is a forum for hydrologists to learn about, recommend, question and discuss new and established, basic and advanced methods and equipment for hydrological research. As a database of "lessons learned" it does not only contain short descriptions of specific experimental equipment but also information on encountered errors and problems and recommendations on how to deal with them. This makes valuable personal field experience accessible to a wider audience. The Wiki allows experimentalists to share and find solutions for common problems and thus helps us in not making the same mistakes others have made before us. At the same time modellers can use this platform to find information on sources of error and uncertainty in the data they use for model validation and calibration. The general idea and layout of the Experimental Hydrology Wiki is presented here along with an invitation to all experimental hydrologists to contribute their knowledge and experiences! http://www.experimental- hydrology.net/

  9. Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable?

    PubMed Central

    Hui, David

    2016-01-01

    Background Prognosis is a key driver of clinical decision-making. However, available prognostication tools have limited accuracy and variable levels of validation. Methods Principles of survival prediction and literature on clinician prediction of survival, prognostic factors, and prognostic models were reviewed, with a focus on patients with advanced cancer and a survival rate of a few months or less. Results The 4 principles of survival prediction are (a) prognostication is a process instead of an event, (b) prognostic factors may evolve over the course of the disease, (c) prognostic accuracy for a given prognostic factor/tool varies by the definition of accuracy, the patient population, and the time frame of prediction, and (d) the exact timing of death cannot be predicted with certainty. Clinician prediction of survival rate is the most commonly used approach to formulate prognosis. However, clinicians often overestimate survival rates with the temporal question. Other clinician prediction of survival approaches, such as surprise and probabilistic questions, have higher rates of accuracy. Established prognostic factors in the advanced cancer setting include decreased performance status, delirium, dysphagia, cancer anorexia–cachexia, dyspnea, inflammation, and malnutrition. Novel prognostic factors, such as phase angle, may improve rates of accuracy. Many prognostic models are available, including the Palliative Prognostic Score, the Palliative Prognostic Index, and the Glasgow Prognostic Score. Conclusions Despite the uncertainty in survival prediction, existing prognostic tools can facilitate clinical decision-making by providing approximated time frames (months, weeks, or days). Future research should focus on clarifying and comparing the rates of accuracy for existing prognostic tools, identifying and validating novel prognostic factors, and linking prognostication to decision-making. PMID:26678976

  10. Development of advanced structural analysis methodologies for predicting widespread fatigue damage in aircraft structures

    NASA Technical Reports Server (NTRS)

    Harris, Charles E.; Starnes, James H., Jr.; Newman, James C., Jr.

    1995-01-01

    NASA is developing a 'tool box' that includes a number of advanced structural analysis computer codes which, taken together, represent the comprehensive fracture mechanics capability required to predict the onset of widespread fatigue damage. These structural analysis tools have complementary and specialized capabilities ranging from a finite-element-based stress-analysis code for two- and three-dimensional built-up structures with cracks to a fatigue and fracture analysis code that uses stress-intensity factors and material-property data found in 'look-up' tables or from equations. NASA is conducting critical experiments necessary to verify the predictive capabilities of the codes, and these tests represent a first step in the technology-validation and industry-acceptance processes. NASA has established cooperative programs with aircraft manufacturers to facilitate the comprehensive transfer of this technology by making these advanced structural analysis codes available to industry.

  11. Computational methods in the prediction of advanced subsonic and supersonic propeller induced noise: ASSPIN users' manual

    NASA Technical Reports Server (NTRS)

    Dunn, M. H.; Tarkenton, G. M.

    1992-01-01

    This document describes the computational aspects of propeller noise prediction in the time domain and the use of high speed propeller noise prediction program ASSPIN (Advanced Subsonic and Supersonic Propeller Induced Noise). These formulations are valid in both the near and far fields. Two formulations are utilized by ASSPIN: (1) one is used for subsonic portions of the propeller blade; and (2) the second is used for transonic and supersonic regions on the blade. Switching between the two formulations is done automatically. ASSPIN incorporates advanced blade geometry and surface pressure modelling, adaptive observer time grid strategies, and contains enhanced numerical algorithms that result in reduced computational time. In addition, the ability to treat the nonaxial inflow case has been included.

  12. An integrated theory for predicting the hydrothermomechanical response of advanced composite structural components

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

    1977-01-01

    A theory is developed for predicting the hydrothermomechanical response of advanced composite structural components. The combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and of angleplied laminates are also evaluated. The materials investigated consist of neat PR-288 epoxy matrix resin and an AS-type graphite fiber/PR-288 resin unidirectional composite.

  13. NOAA Drought Task Force: A Coordinated Research Initiative to Advance Drought Understanding, Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Mariotti, A.; Barrie, D.

    2014-12-01

    The NOAA's Drought Task Force was first established in October 2011 and renewed in October 2014 with the goal of achieving significant new advances in the ability to understand, monitor and predict drought over North America. The Task Force is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in support of the National Integrated Drought Information System NIDIS. The Drought Task Force also represents an important research contribution to efforts to develop an international Global Drought Information System (GDIS). The Drought Task Force brings together leading drought scientists research laboratories and/or operational centers from NOAA, other U.S. agencies laboratories and academia. Their concerted research effort builds on individual MAPP research projects and related drought-research sector developments. The projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those evaluating new drought monitoring and prediction tools for operational and service purposes. This contribution will present an overview of Drought Task Force activities and plans to date, including highlights of research activities and how the group has been working in partnership with NIDIS and synergy with GDIS to advance the science underpinning the development, assessment and provision of drought information.

  14. Advancing the understanding, monitoring and prediction of North American drought in support of NIDIS

    NASA Astrophysics Data System (ADS)

    Mariotti, Annarita; Pulwarty, Roger

    2014-05-01

    The NOAA's Drought Task Force was established in October 2011 with the goal of achieving significant new advances in the ability to understand, monitor and predict drought over North America. The Task Force is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in support of the National Integrated Drought Information System NIDIS. It brings together over thirty-five leading drought scientists research laboratories and/or operational centers from NOAA, other U.S. agencies laboratories and academia. Their concerted research effort builds on individual MAPP research projects and related drought-research sector developments. The projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those evaluating new drought monitoring and prediction tools for operational and service purposes. In this presentation we will show how a coordinated, sustained multidisciplinary effort to assess understanding of both past droughts and emergent events contributes to the effectiveness of early warning systems. This contribution will present an overview of Drought Task Force activities to date, including highlights of research activities and how the group has been working in partnership with NIDIS to advance the science underpinning the development, assessment and provision of drought information.

  15. Measured and predicted rotor performance for the SERI advanced wind turbine blades

    SciTech Connect

    Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

    1992-02-01

    Measured and predicted rotor performance for the SERI advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction. 11 refs.

  16. Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems

    NASA Technical Reports Server (NTRS)

    Kontos, K. B.; Janardan, B. A.; Gliebe, P. R.

    1996-01-01

    Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.

  17. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models

    NASA Astrophysics Data System (ADS)

    Pau, George Shu Heng; Shen, Chaopeng; Riley, William J.; Liu, Yaning

    2016-02-01

    The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.

  18. Recent Advances in Quantifying Hydrological Processes Linking Water, Carbon, and Energy Exports into Coastal Margins Along the Arctic Land-Sea Boundary

    NASA Astrophysics Data System (ADS)

    Rawlins, M. A.

    2014-12-01

    The high northern latitudes have experienced rapid warming in recent decades with projections of larger increases likely by the end of this century. Warming permafrost and an acceleration of the arctic freshwater cycle are among the myriad interconnected changes taking place that have the potential to impact ecosystems throughout the pan-Arctic. The Arctic Ocean receives a disproportionately large amount of global freshwater runoff and as such near-shore coastal margins along the arctic land-sea boundary are strongly influenced by riverine freshwater discharge. Alterations in hydrological flows driven by a changing climate and other perturbations, therefore, are likely to impact the biology and biogeochemistry of arctic coastal margins. Advances have been made in the quantification of water, carbon, and materials transports with recent studies documenting significant changes in exports of quantities such as dissolved organic carbon from large rivers, linked in turn to changes in landscape characteristics and hydrological flow rates. Here key measured data sets, derived empirical relationships, and the resulting pan-Arctic estimates for several constituents are described for the major arctic rivers and full pan-Arctic basin. Complementary estimates from a process-based model are presented, illustrating the potential for leveraging measured data to derive more accurate flows at basin and continental scales. A series of retrospective model simulations point to an increasing influence of river-borne heat transport on ice melt in coastal margins. Case studies of large freshwater anomalies provide a framework for understanding connections between river discharge and the biology and biogeochemistry of arctic coastal margins.

  19. Plastic Instability in Complex Strain Paths Predicted by Advanced Constitutive Equations

    NASA Astrophysics Data System (ADS)

    Butuc, Marilena C.; Barlat, Frédéric; Gracio, José J.; Vincze, Gabriela

    2011-08-01

    The present paper aims at predicting plastic instabilities under complex loading histories using an advanced sheet metal forming limit model. The onset of localized necking is computed using the Marciniak-Kuczinsky (MK) analysis [1] with a physically-based hardening model and the phenomenological anisotropic yield criterion Yld2000-2d [2]. The hardening model accounts for anisotropic work-hardening induced by the microstructural evolution at large strains, which was proposed by Teodosiu and Hu [3]. Simulations are carried out for linear and complex strain paths. Experimentally, two deep-drawing quality sheet metals are selected: a bake-hardening steel (BH) and a DC06 steel sheet. The validity of the model is assessed by comparing the predicted and experimental forming limits. The remarkable accuracy of the developed software to predict the forming limits under linear and non-linear strain path is obviously due to the performance of the advanced constitutive equations to describe with great detail the material behavior. The effect of strain-induced anisotropy on formability evolution under strain path changes, as predicted by the microstructural hardening model, is particularly well captured by the model.

  20. Unsteady blade surface pressures on a large-scale advanced propeller - Prediction and data

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1990-01-01

    An unsteady three dimensional Euler analysis technique is employed to compute the flowfield of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (take-off) the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

  1. Unsteady blade-surface pressures on a large-scale advanced propeller: Prediction and data

    NASA Technical Reports Server (NTRS)

    Nallasamy, M.; Groeneweg, J. F.

    1990-01-01

    An unsteady 3-D Euler analysis technique is employed to compute the flow field of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (takeoff), the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

  2. Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change

    NASA Astrophysics Data System (ADS)

    Wu, J. Y.; Thompson, J. R.; Kolka, R. K.; Franz, K. J.; Stewart, T. W.

    2013-06-01

    Streams are natural features in urban landscapes that can provide ecosystem services for urban residents. However, urban streams are under increasing pressure caused by multiple anthropogenic impacts, including increases in human population and associated impervious surface area, and accelerated climate change. The ability to anticipate these changes and better understand their effects on streams is important for developing and implementing strategies to mitigate potentially negative effects. In this study, stream flow was monitored during April-November (2011 and 2012), and the data were used to apply the Storm Water Management Model (SWMM) for five urban watersheds in central Iowa, USA representing a gradient of percent impervious surface (IS, ranging from 5.3 to 37.1%). A set of three scenarios was designed to quantify hydrological responses to independent and combined effects of climate change (18% increase in precipitation), and land cover change (absolute increases between 5.2 and 17.1%, based on separate projections of impervious surfaces for the five watersheds) for the year 2040 compared to a current condition simulation. An additional set of three scenarios examined stream response to different distributions of land cover change within a single watershed. Hydrological responses were quantified using three indices: unit-area peak discharge, flashiness (R-B Index), and runoff ratio. Stream hydrology was strongly affected by watershed percent IS. For the current condition simulation, values for all three indices were five to seven times greater in the most developed watershed compared to the least developed watershed. The climate change scenario caused a 20.8% increase in unit-area peak discharge on average across the five watersheds compared to the current condition simulation. The land cover change scenario resulted in large increases for all three indices: 49.5% for unit-area peak discharge, 39.3% for R-B Index, and 73.9% for runoff ratio, on average, for

  3. Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change

    NASA Astrophysics Data System (ADS)

    Wu, J. Y.; Thompson, J. R.; Kolka, R. K.; Franz, K. J.; Stewart, T. W.

    2013-12-01

    Streams are natural features in urban landscapes that can provide ecosystem services for urban residents. However, urban streams are under increasing pressure caused by multiple anthropogenic impacts, including increases in human population and associated impervious surface area, and accelerated climate change. The ability to anticipate these changes and better understand their effects on streams is important for developing and implementing strategies to mitigate potentially negative effects. In this study, stream flow was monitored during April-November (2011 and 2012), and the data were used to apply the Storm Water Management Model (SWMM) for five urban watersheds in central Iowa, USA, representing a gradient of percent impervious surface (IS, ranging from 5.3 to 37.1%). A set of three scenarios was designed to quantify hydrological responses to independent and combined effects of climate change (18% increase in precipitation), and land cover change (absolute increases between 5.2 and 17.1%, based on separate projections of impervious surfaces for the five watersheds) for the year 2040 compared to a current condition simulation. An additional set of three scenarios examined stream response to different distributions of land cover change within a single watershed. Hydrological responses were quantified using three indices: unit-area peak discharge, flashiness (R-B Index; Richards-Baker Index), and runoff ratio. Stream hydrology was strongly affected by watershed percent IS. For the current condition simulation, values for all three indices were five to seven times greater in the most developed watershed compared to the least developed watershed. The climate change scenario caused a 20.8% increase in unit-area peak discharge on average across the five watersheds compared to the current condition simulation. The land cover change scenario resulted in large increases for all three indices: 49.5% for unit-area peak discharge, 39.3% for R-B Index, and 73.9% for runoff

  4. Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic Region of the USA: A precursory analysis to hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Siddique, Ridwan; Mejia, Alfonso; Brown, James; Reed, Seann; Ahnert, Peter

    2015-10-01

    Accurate precipitation forecasts are required for accurate flood forecasting. The structures of different precipitation forecasting systems are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of, and accounting for, predictive uncertainty. Hence, routine verification is necessary to understand the quality of forecasts as inputs to hydrologic modeling. In this study, we verify precipitation forecasts from the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2), as well as the 21-member Short Range Ensemble Forecast (SREF) system. Specifically, basin averaged precipitation forecasts are verified for different basin sizes (spatial scales) in the operating domain of the Middle Atlantic River Forecast Center (MARFC), using multi-sensor precipitation estimates (MPEs) as the observed data. The quality of the ensemble forecasts is evaluated conditionally upon precipitation amounts, forecast lead times, accumulation periods, and seasonality using different verification metrics. Overall, both GEFSRv2 and SREF tend to overforecast light to moderate precipitation and underforecast heavy precipitation. In addition, precipitation forecasts from both systems become increasingly reliable with increasing basin size and decreasing precipitation threshold, and the 24-hourly forecasts show slightly better skill than the 6-hourly forecasts. Both systems show a strong seasonal trend, characterized by better skill during the cool season than the warm season. Ultimately, the verification results lead to guidance on the expected quality of the precipitation forecasts, together with an assessment of their relative quality and unique information content, which is useful and necessary for their application in hydrologic forecasting.

  5. Improving Flood Prediction Through the Assimilation of AMSR-E Soil Moisture Retrievals into a Hydrologic Model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Knowledge of antecedent soil moisture conditions provides a key source of predictability for short-term streamflow forecasting. Such knowledge can potentially be retrieved from passive microwave instruments aboard spaceborne satellites. In this study, the marginal benefit of assimilating spaceborn...

  6. Climate Sensitivity Runs and Regional Hydrologic Modeling for Predicting the Response of the Greater Florida Everglades Ecosystem to Climate Change

    NASA Astrophysics Data System (ADS)

    Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

    2015-04-01

    It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida.

  7. Climate sensitivity runs and regional hydrologic modeling for predicting the response of the greater Florida Everglades ecosystem to climate change.

    PubMed

    Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

    2015-04-01

    It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida. PMID:25011530

  8. A Simple Tool to Predict ESRD Within 1 Year in Elderly Patients with Advanced CKD

    PubMed Central

    Drawz, Paul E.; Goswami, Puja; Azem, Reem; Babineau, Denise C.; Rahman, Mahboob

    2013-01-01

    BACKGROUND/OBJECTIVES Chronic kidney disease (CKD) is common in older patients; currently, no tools are available to predict the risk of end-stage renal disease (ESRD) within 1 year. The goal of this study was to develop and validate a model to predict the 1 year risk for ESRD in elderly subjects with advanced CKD. DESIGN Retrospective study SETTING Veterans Affairs Medical Center PARTICIPANTS Patients over 65 years of age with CKD with an estimated (eGFR) less than 30mL/min/1.73m2. MEASUREMENTS The outcome was ESRD within 1 year of the index eGFR. Cox regression was used to develop a predictive model (VA risk score) which was validated in a separate cohort. RESULTS Of the 1,866 patients in the developmental cohort, 77 developed ESRD. Risk factors for ESRD in the final model were age, congestive heart failure, systolic blood pressure, eGFR, potassium, and albumin. In the validation cohort, the C index for the VA risk score was 0.823. The risk for developing ESRD at 1 year from lowest to highest tertile was 0.08%, 2.7%, and 11.3% (P<0.001). The C-index for the recently published Tangri model in the validation cohort was 0.780. CONCLUSION A new model using commonly available clinical measures shows excellent ability to predict the onset of ESRD within the next year in elderly subjects. Additionally, the Tangri model had very good predictive ability. Patients and physicians can use these risk models to inform decisions regarding preparation for renal replacement therapy in patients with advanced CKD. PMID:23617782

  9. Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

    NASA Technical Reports Server (NTRS)

    Wilson, Scott D.; Reid, Terry V.; Schifer, Nicholas A.; Briggs, Maxwell H.

    2012-01-01

    The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two high-efficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. Microporous bulk insulation is used in the ground support test hardware to minimize the loss of thermal energy from the electric heat source to the environment. The insulation package is characterized before operation to predict how much heat will be absorbed by the convertor and how much will be lost to the environment during operation. In an effort to validate these predictions, numerous tasks have been performed, which provided a more accurate value for net heat input into the ASCs. This test and modeling effort included: (a) making thermophysical property measurements of test setup materials to provide inputs to the numerical models, (b) acquiring additional test data that was collected during convertor tests to provide numerical models with temperature profiles of the test setup via thermocouple and infrared measurements, (c) using multidimensional numerical models (computational fluid dynamics code) to predict net heat input of an operating convertor, and (d) using validation test hardware to provide direct comparison of numerical results and validate the multidimensional numerical models used to predict convertor net heat input. This effort produced high fidelity ASC net heat input predictions, which were successfully validated using

  10. Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

    PubMed Central

    Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

    2015-01-01

    Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

  11. Comparison of two imaging programs in predicting the soft tissue changes with mandibular advancement surgery.

    PubMed

    Ravindranath, Sneha; Krishnaswamy, Nathamuni Rengarajan; Sundaram, Venkateswaran

    2011-01-01

    Establishing common objectives and expectations concerning the outcome of proposed surgical orthodontic therapy is a crucial part of the treatment planning process, which has been greatly simplified by imaging software. The purpose of this study was to investigate the reliability of two surgical imaging programs--Dolphin Imaging 10 and Vistadent OC--in simulating the actual outcome of mandibular advancement surgery by using a visual analog scale (VAS) judged by a panel of orthodontists, oral surgeons, and laypersons. The predictions were also analyzed with soft tissue cephalometric evaluation. The results of the study showed that in predicting the surgical outcome evaluated by the VAS, both programs received a mean rating of fair. One was marginally superior for the overall assessment among all three panelist groups. Region-wise, rating indicated the lower lip region to be the least accurate, and the submental region received the highest scores. The soft tissue cephalometric parameters showed minimal differences except for the lower lip parameters. Thus, Dolphin Imaging 10 and Vistadent OC are reliable in predicting mandibular advancement surgical outcomes with inaccuracies chiefly in the lower lip region. PMID:22299108

  12. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

    USGS Publications Warehouse

    Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

    2009-01-01

    This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

  13. Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Tam, Chi-Yung

    2016-05-01

    Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model ensemble (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based downscaling method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea

  14. Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Tam, Chi-Yung

    2015-07-01

    Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model ensemble (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based downscaling method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea

  15. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

    NASA Astrophysics Data System (ADS)

    Viney, Neil R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B. F. W.; Frede, H.; Gräff, T.; Hubrechts, L.; Huisman, J. A.; Jakeman, A. J.; Kite, G. W.; Lanini, J.; Leavesley, G.; Lettenmaier, D. P.; Lindström, G.; Seibert, J.; Sivapalan, M.; Willems, P.

    2009-02-01

    This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

  16. Recent advances in dynamical extra-seasonal to annual climate prediction at IAP/CAS

    NASA Astrophysics Data System (ADS)

    Lin, Zhaohui; Wang, Huijun; Zhou, Guangqing; Chen, Hong; Lang, Xianmei; Zhao, Yan; Zeng, Qingcun

    2004-06-01

    Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980 1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998 2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

  17. Simulation studies of the impact of advanced observing systems on numerical weather prediction

    NASA Technical Reports Server (NTRS)

    Atlas, R.; Kalnay, E.; Susskind, J.; Reuter, D.; Baker, W. E.; Halem, M.

    1984-01-01

    To study the potential impact of advanced passive sounders and lidar temperature, pressure, humidity, and wind observing systems on large-scale numerical weather prediction, a series of realistic simulation studies between the European Center for medium-range weather forecasts, the National Meteorological Center, and the Goddard Laboratory for Atmospheric Sciences is conducted. The project attempts to avoid the unrealistic character of earlier simulation studies. The previous simulation studies and real-data impact tests are reviewed and the design of the current simulation system is described. Consideration is given to the simulation of observations of space-based sounding systems.

  18. Development of a constitutive model for creep and life prediction of advanced silicon nitride ceramics

    SciTech Connect

    Ding, J.L.; Liu, K.C.; Brinkman, C.R.

    1992-12-31

    A constitutive model capable of describing deformation and predicting rupture life was developed for high temperature ceramic materials under general thermal-mechanical loading conditions. The model was developed based on the deformation and fracture behavior observed from a systematic experimental study on an advanced silicon nitride (Si{sub 3}N{sub 4}) ceramic material. Validity of the model was evaluated with reference to creep and creep rupture data obtained under constant and stepwise-varied loading conditions, including the effects of annealing on creep and creep rupture behavior.

  19. A proposed predictive model for advanced fibrosis in patients with chronic hepatitis B and its validation

    PubMed Central

    Nishikawa, Hiroki; Hasegawa, Kunihiro; Ishii, Akio; Takata, Ryo; Enomoto, Hirayuki; Yoh, Kazunori; Kishino, Kyohei; Shimono, Yoshihiro; Iwata, Yoshinori; Nakano, Chikage; Nishimura, Takashi; Aizawa, Nobuhiro; Sakai, Yoshiyuki; Ikeda, Naoto; Takashima, Tomoyuki; Iijima, Hiroko; Nishiguchi, Shuhei

    2016-01-01

    Abstract We created a predictive model using serum-based biomarkers for advanced fibrosis (F3 or more) in patients with chronic hepatitis B (CHB) and to confirm the accuracy in an independent cohort. A total of 249 CHB patients were analyzed. To achieve our study aim, a training group (n = 125) and a validation group (n = 124) were formed. In the training group, parameters related to the presence of advanced fibrosis in univariate and multivariate analyses were examined, and a formula for advanced fibrosis was created. Next, we verified the applicability of the predictive model in the validation group. Multivariate analysis identified that gamma-glutamyl transpeptidase (GGT, P = 0.0343) and platelet count (P = 0.0034) were significant predictors of the presence of advanced fibrosis, while Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA+-M2BP, P = 0.0741) and hyaluronic acid (P = 0.0916) tended to be significant factors. Using these 4 parameters, we created the following formula: GMPH score = −0.755 − (0.015 × GGT) − (0.268 × WFA+-M2BP) + (0.167 × platelet count) + (0.003 × hyaluronic acid). In 8 analyzed variables (WFA+-M2BP, aspartate aminotransferase-to-platelet ratio index, FIB-4 index, prothrombin time, platelet count, hyaluronic acid, Forns index, and GMPH score), GMPH score had the highest area under the receiver operating characteristic (AUROC) curve for advanced fibrosis with a value of 0.8064 in the training group and in the validation group, GMPH score also had the highest AUROC (0.7782). In all subgroup analyses of the hepatitis B virus (HBV) status (HB surface antigen quantification, HBV-DNA quantification, and HBe antigen seropositivity), GMPH score in F3 or F4 was significantly lower than that in F0 to F2. In the above mentioned 8 variables, differences between the liver fibrosis stages (F0 to F1 vs F2, F2 vs F3, F3 vs F4, F0 to F1 vs F3, F0 to F1 vs F4, and F2 vs

  20. A proposed predictive model for advanced fibrosis in patients with chronic hepatitis B and its validation.

    PubMed

    Nishikawa, Hiroki; Hasegawa, Kunihiro; Ishii, Akio; Takata, Ryo; Enomoto, Hirayuki; Yoh, Kazunori; Kishino, Kyohei; Shimono, Yoshihiro; Iwata, Yoshinori; Nakano, Chikage; Nishimura, Takashi; Aizawa, Nobuhiro; Sakai, Yoshiyuki; Ikeda, Naoto; Takashima, Tomoyuki; Iijima, Hiroko; Nishiguchi, Shuhei

    2016-08-01

    We created a predictive model using serum-based biomarkers for advanced fibrosis (F3 or more) in patients with chronic hepatitis B (CHB) and to confirm the accuracy in an independent cohort.A total of 249 CHB patients were analyzed. To achieve our study aim, a training group (n = 125) and a validation group (n = 124) were formed. In the training group, parameters related to the presence of advanced fibrosis in univariate and multivariate analyses were examined, and a formula for advanced fibrosis was created. Next, we verified the applicability of the predictive model in the validation group.Multivariate analysis identified that gamma-glutamyl transpeptidase (GGT, P = 0.0343) and platelet count (P = 0.0034) were significant predictors of the presence of advanced fibrosis, while Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA-M2BP, P = 0.0741) and hyaluronic acid (P = 0.0916) tended to be significant factors. Using these 4 parameters, we created the following formula: GMPH score = -0.755 - (0.015 × GGT) - (0.268 × WFA-M2BP) + (0.167 × platelet count) + (0.003 × hyaluronic acid). In 8 analyzed variables (WFA-M2BP, aspartate aminotransferase-to-platelet ratio index, FIB-4 index, prothrombin time, platelet count, hyaluronic acid, Forns index, and GMPH score), GMPH score had the highest area under the receiver operating characteristic (AUROC) curve for advanced fibrosis with a value of 0.8064 in the training group and in the validation group, GMPH score also had the highest AUROC (0.7782). In all subgroup analyses of the hepatitis B virus (HBV) status (HB surface antigen quantification, HBV-DNA quantification, and HBe antigen seropositivity), GMPH score in F3 or F4 was significantly lower than that in F0 to F2. In the above mentioned 8 variables, differences between the liver fibrosis stages (F0 to F1 vs F2, F2 vs F3, F3 vs F4, F0 to F1 vs F3, F0 to F1 vs F4, and F2 vs F4) for the entire

  1. Solid phase evolution in the Biosphere 2 hillslope experiment as predicted by modeling of hydrologic and geochemical fluxes

    SciTech Connect

    Dontsova, K.; Steefel, C.I.; Desilets, S.; Thompson, A.; Chorover, J.

    2009-07-15

    A reactive transport geochemical modeling study was conducted to help predict the mineral transformations occurring over a ten year time-scale that are expected to impact soil hydraulic properties in the Biosphere 2 (B2) synthetic hillslope experiment. The modeling sought to predict the rate and extent of weathering of a granular basalt (selected for hillslope construction) as a function of climatic drivers, and to assess the feedback effects of such weathering processes on the hydraulic properties of the hillslope. Flow vectors were imported from HYDRUS into a reactive transport code, CrunchFlow2007, which was then used to model mineral weathering coupled to reactive solute transport. Associated particle size evolution was translated into changes in saturated hydraulic conductivity using Rosetta software. We found that flow characteristics, including velocity and saturation, strongly influenced the predicted extent of incongruent mineral weathering and neo-phase precipitation on the hillslope. Results were also highly sensitive to specific surface areas of the soil media, consistent with surface reaction controls on dissolution. Effects of fluid flow on weathering resulted in significant differences in the prediction of soil particle size distributions, which should feedback to alter hillslope hydraulic conductivities.

  2. Predictive and preventive strategies to advance the treatments of cardiovascular and cerebrovascular diseases: the Ukrainian context

    PubMed Central

    2012-01-01

    Despite great efforts in treatments of cardiovascular diseases, the field requires innovative strategies because of high rates of morbidity, mortality and disability, indicating evident deficits in predictive vascular diagnosis and individualized treatment approaches. Talking about the vascular system, currently, physicians are not provided with integrated medical approaches to diagnose and treat vascular diseases. Only an individual global approach to the analysis of all segments in the vascular system of a patient allows finding the optimal way for vascular disease treatment. As for the existing methodology, there is a dominance of static methods such as X-ray contrast angiography and magnetic resonance imaging in angiomode. Taking into account the world experience, this article deals with innovative strategies, aiming at predictive diagnosis in vascular system, personalization of the biomedical treatment approaches, and targeted prevention of individual patient cohorts. Clinical examples illustrate the advances in corresponding healthcare sectors. Recommendations are provided to promote the field. PMID:23083430

  3. Assessment of potential for small hydro/solar power integration in a mountainous, data sparse region: the role of hydrological prediction accuracy

    NASA Astrophysics Data System (ADS)

    Borga, Marco; Francois, Baptiste; Creutin, Jean-Dominique; Hingray, Benoit; Zoccatelli, Davide; Tardivo, Gianmarco

    2015-04-01

    In many parts of the world, integration of small hydropower and solar/wind energy sources along river systems is examined as a way to meet pressing renewable energy targets. Depending on the space and time scales considered, hydrometeorological variability may synchronize or desynchronize solar/wind, runoff and the demand opening the possibility to use their complementarity to smooth the intermittency of each individual energy source. Rivers also provide important ecosystem services, including the provision of high quality downstream water supply and the maintenance of in-stream habitats. With future supply and demand of water resources both impacted by environmental change, a good understanding of the potential for the integration among hydropower and solar/wind energy sources in often sparsely gauged catchments is important. In such cases, where complex data-demanding models may be inappropriate, there is a need for simple conceptual modelling approaches that can still capture the main features of runoff generation and artificial regulation processes. In this work we focus on run-of-the-river and solar-power interaction assessment. In order to catch the three key cycles of the load fluctuation - daily, weekly and seasonal, the time step used in the study is the hourly resolution. We examine the performance of a conceptual hydrological model which includes facilities to model dam regulation and diversions and hydrological modules to account for the effect of glaciarised catchments. The model is applied to catchments of the heavily regulated Upper Adige river system (6900 km2), Eastern Italian Alps, which has a long history of hydropower generation. The model is used to characterize and predict the natural flow regime, assess the regulation impacts, and simulate co-fluctuations between run-of- the-river and solar power. The results demonstrates that the simple, conceptual modelling approach developed here can capture the main hydrological and regulation processes

  4. Multiscale Modeling of Advanced Materials for Damage Prediction and Structural Health Monitoring

    NASA Astrophysics Data System (ADS)

    Borkowski, Luke

    Advanced aerospace materials, including fiber reinforced polymer and ceramic matrix composites, are increasingly being used in critical and demanding applications, challenging the current damage prediction, detection, and quantification methodologies. Multiscale computational models offer key advantages over traditional analysis techniques and can provide the necessary capabilities for the development of a comprehensive virtual structural health monitoring (SHM) framework. Virtual SHM has the potential to drastically improve the design and analysis of aerospace components through coupling the complementary capabilities of models able to predict the initiation and propagation of damage under a wide range of loading and environmental scenarios, simulate interrogation methods for damage detection and quantification, and assess the health of a structure. A major component of the virtual SHM framework involves having micromechanics-based multiscale composite models that can provide the elastic, inelastic, and damage behavior of composite material systems under mechanical and thermal loading conditions and in the presence of microstructural complexity and variability. Quantification of the role geometric and architectural variability in the composite microstructure plays in the local and global composite behavior is essential to the development of appropriate scale-dependent unit cells and boundary conditions for the multiscale model. Once the composite behavior is predicted and variability effects assessed, wave-based SHM simulation models serve to provide knowledge on the probability of detection and characterization accuracy of damage present in the composite. The research presented in this dissertation provides the foundation for a comprehensive SHM framework for advanced aerospace materials. The developed models enhance the prediction of damage formation as a result of ceramic matrix composite processing, improve the understanding of the effects of architectural and

  5. A hybrid numerical technique for predicting the aerodynamic and acoustic fields of advanced turboprops

    NASA Technical Reports Server (NTRS)

    Homicz, G. F.; Moselle, J. R.

    1985-01-01

    A hybrid numerical procedure is presented for the prediction of the aerodynamic and acoustic performance of advanced turboprops. A hybrid scheme is proposed which in principle leads to a consistent simultaneous prediction of both fields. In the inner flow a finite difference method, the Approximate-Factorization Alternating-Direction-Implicit (ADI) scheme, is used to solve the nonlinear Euler equations. In the outer flow the linearized acoustic equations are solved via a Boundary-Integral Equation (BIE) method. The two solutions are iteratively matched across a fictitious interface in the flow so as to maintain continuity. At convergence the resulting aerodynamic load prediction will automatically satisfy the appropriate free-field boundary conditions at the edge of the finite difference grid, while the acoustic predictions will reflect the back-reaction of the radiated field on the magnitude of the loading source terms, as well as refractive effects in the inner flow. The equations and logic needed to match the two solutions are developed and the computer program implementing the procedure is described. Unfortunately, no converged solutions were obtained, due to unexpectedly large running times. The reasons for this are discussed and several means to alleviate the situation are suggested.

  6. Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

    USGS Publications Warehouse

    Fienen, Michael N.; Doherty, John E.; Hunt, Randall J.; Reeves, Howard W.

    2010-01-01

    The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

  7. Hydrologic impacts of thawing permafrost—A review

    USGS Publications Warehouse

    Walvoord, Michelle Ann; Kurylyk, Barret L.

    2016-01-01

    Where present, permafrost exerts a primary control on water fluxes, flowpaths, and distribution. Climate warming and related drivers of soil thermal change are expected to modify the distribution of permafrost, leading to changing hydrologic conditions, including alterations in soil moisture, connectivity of inland waters, streamflow seasonality, and the partitioning of water stored above and below ground. The field of permafrost hydrology is undergoing rapid advancement with respect to multiscale observations, subsurface characterization, modeling, and integration with other disciplines. However, gaining predictive capability of the many interrelated consequences of climate change is a persistent challenge due to several factors. Observations of hydrologic change have been causally linked to permafrost thaw, but applications of process-based models needed to support and enhance the transferability of empirical linkages have often been restricted to generalized representations. Limitations stem from inadequate baseline permafrost and unfrozen hydrogeologic characterization, lack of historical data, and simplifications in structure and process representation needed to counter the high computational demands of cryohydrogeologic simulations. Further, due in part to the large degree of subsurface heterogeneity of permafrost landscapes and the nonuniformity in thaw patterns and rates, associations between various modes of permafrost thaw and hydrologic change are not readily scalable; even trajectories of change can differ. This review highlights promising advances in characterization and modeling of permafrost regions and presents ongoing research challenges toward projecting hydrologic and ecologic consequences of permafrost thaw at time and spatial scales that are useful to managers and researchers.

  8. Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

    NASA Astrophysics Data System (ADS)

    Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

    2015-12-01

    Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we

  9. Snow depth estimation, structure, prediction, and hydrologic modeling at the kilometer scale in the Colorado Rocky Mountains

    NASA Astrophysics Data System (ADS)

    McCreight, James L.

    Research focuses on observing and predicting spatial distribution of snow depth at the kilometer scale. Observation of spatial snow depth distribution is considered by its estimation from random, sparse observations and important factors affecting this estimation. Predicting spatial distribution of both snow depth and melt rates begins from simple hypothesis wherein the spatial distribution of snow depth is structured by the spatial distribution of controlling variables. Predictions made by this structured view are evaluated in spatial modeling of peak-accumulation snow depth and applied to spatial distribution of a point-scale, temperature-index model of snowmelt runoff using minimal parameter complexity. High-resolution light detection and ranging (LiDAR) measurements provide a rich backdrop for understanding estimation from sparse observations and developing our structured view of snow distribution. The data are used to illuminate the effects of sample size on estimation skill, the uncertainty in estimation due to random sampling, the effect of model resolution on estimation skill, and the difference between cross-validated skill and skill based on the entire distribution. None of these topics have previously been explored in the literature. The effect of predictor quality is also investigated. LiDAR derived predictors are compared to readily available predictors downloaded from the internet. Hierarchical cluster analysis is used to decompose spatial non-stationarity of snow depth and results match qualitative understanding of the spatial distribution of physical controls. The same methodology is then used to decompose spatial non-stationarity of physical controls and infer patterns of snow depth distribution independent of observations. Even when using readily-available predictors, predicted patterns require at least 100--200 observations to be matched by standard estimation methods. Predicted patterns are then applied to formulate a parameterized spatial

  10. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

    PubMed

    Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

    2015-06-30

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  11. A Model to Predict Nitrogen Losses in Advanced Soil-Based Wastewater Treatment Systems

    NASA Astrophysics Data System (ADS)

    Morales, I.; Cooper, J.; Loomis, G.; Kalen, D.; Amador, J.; Boving, T. B.

    2014-12-01

    Most of the non-point source Nitrogen (N) load in rural areas is attributed to onsite wastewater treatment systems (OWTS). Nitrogen compounds are considered environmental pollutants because they deplete the oxygen availability in water bodies and produce eutrophication. The objective of this study was to simulate the fate and transport of Nitrogen in OWTS. The commercially-available 2D/3D HYDRUS software was used to develop a transport and fate model. Experimental data from a laboratory meso-cosm study included the soil moisture content, NH4 and NO3- data. That data set was used to calibrate the model. Three types of OWTS were simulated: (1) pipe-and-stone (P&S), (2) advanced soil drainfields, pressurized shallow narrow drainfield (SND) and (3) Geomat (GEO), a variation of SND. To better understand the nitrogen removal mechanism and the performance of OWTS technologies, replicate (n = 3) intact soil mesocosms were used with 15N-labelled nitrogen inputs. As a result, it was estimated that N removal by denitrification was predominant in P&S. However, it is suggested that N was removed by nitrification in SND and GEO. The calibrated model was used to estimate Nitrogen fluxes for both conventional and advanced OWTS. Also, the model predicted the N losses from nitrification and denitrification in all OWTS. These findings help to provide practitioners with guidelines to estimate N removal efficiencies for OWTS, and predict N loads and spatial distribution for identifying non-point sources.

  12. Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer

    PubMed Central

    Du, Meijun; Dittmar, Rachel L.; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R.; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L.; Huang, Chiang-Ching; Wang, Liang

    2015-01-01

    Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer. PMID:25915538

  13. COMBINING CLIMATE MODEL PREDICTIONS, HYDROLOGICAL MODELING, AND ECOLOGICAL NICHE MODELING ALGORITHMS TO PREDICT THE IMPACTS OF CLIMATE CHANGE ON AQUATIC BIODIVERSITY

    EPA Science Inventory

    The results of this research will provide a broad taxonomic and regional assessment of the impacts of climate change on aquatic species in the United States by producing predictions of current and future habitat quality for aquatic taxa based on multiple climate change scen...

  14. A prospective study of the efficacy of magnetic resonance spectroscopy imaging for predicting locally advanced prostate cancer

    PubMed Central

    Razi, Ali; Parizi, Mehdi Kardoust; Kazemeini, Seid Mohammad; Abedi, Akbar

    2015-01-01

    Objective: To evaluate the efficacy of magnetic resonance spectroscopy imaging (MRSI) for predicting locally advanced prostate cancer (PC). Materials and methods: Between April 2009 and July 2012, 80 consecutive patients with clinically localized PC had undergone endorectal MRSI before radical retropubic prostatectomy. Clinicopathological parameters, including age, preoperative prostate-specific antigen (PSA), Gleason score (GS) at biopsy, perinural invasion at biopsy, prostate weight at surgery, GS of surgical specimen, and pathological staging were recorded. The MRSI findings were compared with the histopathological findings of the radical prostatectomy. The diagnostic accuracy measures consisting of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of MRSI, and other variables in the diagnosis of locally advanced PC (Pathology Stages pT3a, pT3b, or pT4) were evaluated. Results: Sensitivity, specificity, PPV, and NPV of MRSI in detecting locally advanced PC is 42.4%, 93.6%, 82.3%, and 69.8%, respectively [area under the receiver operating characteristic (ROC) curve=0.658, p value <0.0001]. MRSI, cancer-positive core percentage at biopsy, and GS at biopsy are more accurate factors among all the predictive variables in predicting locally advanced PC. Conclusion: MRSI may be considered as a complementary diagnostic modality with high specificity and moderate sensitivity in predicting locally advanced PC. Combination of this modality with other predictive factors helps the surgeon and patient to select an appropriate treatment strategy. PMID:26328204

  15. Hydrology, society, change and uncertainty

    NASA Astrophysics Data System (ADS)

    Koutsoyiannis, Demetris

    2014-05-01

    Heraclitus, who predicated that "panta rhei", also proclaimed that "time is a child playing, throwing dice". Indeed, change and uncertainty are tightly connected. The type of change that can be predicted with accuracy is usually trivial. Also, decision making under certainty is mostly trivial. The current acceleration of change, due to unprecedented human achievements in technology, inevitably results in increased uncertainty. In turn, the increased uncertainty makes the society apprehensive about the future, insecure and credulous to a developing future-telling industry. Several scientific disciplines, including hydrology, tend to become part of this industry. The social demand for certainties, no matter if these are delusional, is combined by a misconception in the scientific community confusing science with uncertainty elimination. However, recognizing that uncertainty is inevitable and tightly connected with change will help to appreciate the positive sides of both. Hence, uncertainty becomes an important object to study, understand and model. Decision making under uncertainty, developing adaptability and resilience for an uncertain future, and using technology and engineering means for planned change to control the environment are important and feasible tasks, all of which will benefit from advancements in the Hydrology of Uncertainty.

  16. Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

    PubMed Central

    Ribay, Kathryn; Kim, Marlene T.; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

    2016-01-01

    Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR

  17. Early Identification of Students Predicted to Enroll in Advanced, Upper-Level High School Courses: An Examination of Validity

    ERIC Educational Resources Information Center

    DeRose, Diego S.; Clement, Russell W.

    2011-01-01

    Broward County Public Schools' Research Services department uses logistic regression analysis to compute an indicator to predict student enrollment in advanced high school courses, for students entering ninth grade for the first time. This prediction indicator, along with other student characteristics, supports high school guidance staffs in…

  18. Advance Prediction of the March 11, 2011 Great East Japan Earthquake: A Missed Opportunity for Disaster Preparedness

    NASA Astrophysics Data System (ADS)

    Davis, C. A.; Keilis-Borok, V. I.; Kossobokov, V. G.; Soloviev, A.

    2012-12-01

    There was a missed opportunity for implementing important disaster preparedness measures following an earthquake prediction that was announced as an alarm in mid-2001. This intermediate-term middle-range prediction was the initiation of a chain of alarms that successfully detected the time, region, and magnitude range for the magnitude 9.0 March 11, 2011 Great East Japan Earthquake. The prediction chains were made using an algorithm called M8 and is the latest of many predictions tested worldwide for more than 25 years, the results of which show at least a 70% success rate. The earthquake detection could have been utilized to implement measures and improve earthquake preparedness in advance; unfortunately this was not done, in part due to the predictions' limited distribution and the lack of applying existing methods for using intermediate-term predictions to make decisions for taking action. The resulting earthquake and induced tsunami caused tremendous devastation to north-east Japan. Methods that were known in advance of the predication and further advanced during the prediction timeframe are presented in a scenario describing some possibilities on how the 2001 prediction may have been utilized to reduce significant damage, including damage to the Fukushima nuclear power plant, and to show prudent cost-effective actions can be taken if the prediction certainty is known, but not necessarily high. The purpose of this presentation is to show how the prediction information can be strategically used to enhance disaster preparedness and reduce future impacts from the world's largest earthquakes.

  19. The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

    NASA Astrophysics Data System (ADS)

    Lu, Z. L.; Li, D. C.; Lu, B. H.; Zhang, A. F.; Zhu, G. X.; Pi, G.

    2010-05-01

    Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.

  20. Assessing the model performance of an integrated hydrological and biogeochemical model for discharge and nitrate load predictions

    NASA Astrophysics Data System (ADS)

    Pohlert, T.; Breuer, L.; Huisman, J. A.; Frede, H.-G.

    2007-03-01

    In this study, we evaluate the performance of the SWAT-N model, a modified version of the widely used SWAT version, for discharge and nitrate predictions at the mesoscale Dill catchment (Germany) for a 5-year period. The underlying question is, whether the model efficiency is sufficient for scenario analysis of land-use changes on both water quantity and quality. The Shuffled Complex Evolution (SCE-UA) algorithm is used to calibrate the model for daily discharge at the catchments outlet. Model performance is assessed with a split-sampling as well as a proxy-basin test using recorded hydrographs of four additional gauges located within the catchment. The efficiency regarding nitrate load simulation is assessed without further calibration on a daily, log-daily, weekly, and monthly basis as compared to observations derived from an intensive sampling campaign conducted at the catchments outlet. A new approach is employed to test the spatial consistency of the model, where simulated longitudinal profiles of nitrate concentrations were compared with observed longitudinal profiles. It is concluded that the model efficiency of SWAT-N is sufficient for the assessment of scenarios for daily discharge predictions. SWAT-N can be employed without further calibration for nitrate load simulations on both a weekly and monthly basis with an acceptable degree of accuracy. However, the model efficiency for daily nitrate load is insufficient, which can be attributed to both data uncertainty (i.e. point-source effluents and actual farming practise) as well as structural errors. The simulated longitudinal profiles meet the observations reasonably well, which suggests that the model is spatially consistent.

  1. Assessing the model performance of an integrated hydrological and biogeochemical model for discharge and nitrate load predictions

    NASA Astrophysics Data System (ADS)

    Pohlert, T.; Breuer, L.; Huisman, J. A.; Frede, H.-G.

    2006-09-01

    In this study, we evaluate the performance of the SWAT-N model, a modified version of the widely used SWAT version, for discharge and nitrate predictions at the mesoscale Dill catchment for a 5-year period. The underlying question is, whether the model efficiency is sufficient for scenario analysis of land-use changes on both water quantity and quality. The Shuffled Complex Evolution (SCE-UA) algorithm is used to calibrate the model for daily discharge at the catchments outlet. Model performance is assessed with a split-sampling as well as a proxy-basin test using recorded hydrographs of four additional gauges located within the catchment. The efficiency regarding nitrate load simulation is assessed without further calibration on a daily, log-daily, weekly, and monthly basis as compared to observations derived from an intensive sampling campaign conducted at the catchments outlet. A new approach is employed to test the spatial consistency of the model, where simulated longitudinal profiles of nitrate concentrations were compared with observed longitudinal profiles. It is concluded that the model efficiency of SWAT-N is sufficient for the assessment of scenarios for daily discharge predictions. SWAT-N can be employed without further calibration for nitrate load simulations on both a weekly and monthly basis with an acceptable degree of accuracy. However, the model efficiency for daily nitrate load is insufficient, which can be attributed to both data uncertainty (i.e. point-source effluents and actual farming practise) as well as structural errors. The simulated longitudinal profiles meet the observations reasonably well, which suggests that the model is spatially consistent.

  2. The Source Physics Experiments and Advances in Seismic Explosion Monitoring Predictive Capabilities

    NASA Astrophysics Data System (ADS)

    Walter, W. R.; Ford, S. R.; Antoun, T.; Pitarka, A.; Xu, H.; Vorobiev, O.; Rodgers, A.; Pyle, M. L.

    2012-12-01

    Despite many years of study, a number of seismic explosion phenomena remain incompletely understood. These include the generation of S-waves, the variation of absolute amplitudes with emplacement media differences, and the occasional generation of reversed Rayleigh waves. Advances in numerical methods and increased computational power have improved the physics contained in the modeling software and it is possible to couple non-linear source-region effects to far-field propagation codes to predict seismic observables, thereby allowing end-to-end modeling. However, despite the many sensor records from prior nuclear tests, the data available to develop and validate the simulation codes remain limited in important ways. This is particularly the case for the range of both scaled depths of burial and of source media, especially where full near-field to far-field records are available along with key quantitative parameter data such as depth, material properties and yield. For example, two of the most widely used seismic source models, both derived from the best empirical data, Mueller and Murphy (1971) and Denny and Johnson (1989), predict very different amplitudes for greatly overburied explosions. To provide new data to advance predictive explosion modeling capabilities, the National Nuclear Security Administration (NNSA) is carrying out a series of seven chemical explosions over a range of depths and sizes in the Source Physics Experiments (SPE). These shots are taking place in the Climax Stock granite at the Nevada National Security Site, the location where reversed Rayleigh waves from a nuclear test were first observed in the 1962 HARDHAT event (e.g. Brune and Pomeroy, 1963). Three of the SPE shots have successfully occurred so far, and were well-recorded by an extensive set of instrumentation including seismic, acoustic, EM, and remote sensing. In parallel, detailed site characterization has been conducted using geologic mapping and sampling, borehole geophysics

  3. Hillslope hydrology and stability

    USGS Publications Warehouse

    Lu, Ning; Godt, Jonathan

    2012-01-01

    Landslides are caused by a failure of the mechanical balance within hillslopes. This balance is governed by two coupled physical processes: hydrological or subsurface flow and stress. The stabilizing strength of hillslope materials depends on effective stress, which is diminished by rainfall. This book presents a cutting-edge quantitative approach to understanding hydro-mechanical processes across variably saturated hillslope environments and to the study and prediction of rainfall-induced landslides. Topics covered include historic synthesis of hillslope geomorphology and hydrology, total and effective stress distributions, critical reviews of shear strength of hillslope materials and different bases for stability analysis. Exercises and homework problems are provided for students to engage with the theory in practice. This is an invaluable resource for graduate students and researchers in hydrology, geomorphology, engineering geology, geotechnical engineering and geomechanics and for professionals in the fields of civil and environmental engineering and natural hazard analysis.

  4. The Use of Remote Sensing for Monitoring, Prediction, and Management of Hydrologic, Agricultural, and Ecological Processes in the Northern Great Plains

    NASA Technical Reports Server (NTRS)

    Farwell, Sherry O.; DeTroye, Diane (Technical Monitor)

    2002-01-01

    The NASA-EPSCoR program in South Dakota is focused on the enhancement of NASA-related research in earth system science and corresponding infrastructure development to support this theme. Hence, the program has adopted a strategy that keys on research projects that: a) establish quantitative links between geospatial information technologies and fundamental climatic and ecosystem processes in the Northern Great Plains (NGP) and b) develop and use coupled modeling tools, which can be initialized by data from combined satellite and surface measurements, to provide reliable predictions and management guidance for hydrologic, agricultural, and ecological systems of the NGP. Building a partnership network that includes both internal and external team members is recognized as an essential element of the SD NASA-EPSCoR program. Hence, promoting and tracking such linkages along with their relevant programmatic consequences are used as one metric to assess the program's progress and success. This annual report first summarizes general activities and accomplishments, and then provides progress narratives for the two separate, yet related research projects that are essential components of the SD NASA-EPSCoR program.

  5. Three Dimensional Finite Difference Time Domain Modeling of Ground Penetrating Radar with an Efficient and Robust Algorithm to Define and Predict Hydrologic Properties in the Subsurface

    NASA Astrophysics Data System (ADS)

    Eyuboglu, S.; Daniels, J. J.; Pyke, K.

    2005-12-01

    Ground Penetrating Radar (GPR) is a commonly used non-invasive tool to characterize the physical properties of the subsurface. The translation of the physical measurements to geologic and hydrogeologic conditions is the culmination of many geophysical investigations. Numerical modeling increases the applicability of GPR in the geophysics area when applied parallel to the GPR data, allowing to understand the effects of complex electromagnetic phenomena by defining and solving problems, as well as predicting the performance of radar in a complex heterogeneous environment. Finite difference time domain (FDTD) has been widely used for numerical modeling of GPR, but most of the previous algorithms are limited in their ability to model the electrical conductivity and permittivity. In this research, a highly efficient robust algorithm was developed to enhance the effectiveness of the FDTD forward modeling in surroundings characterized by an arbitrary distribution of all electrical properties in three dimensional space. The modeling algorithm was developed for a heterogeneous half-space medium to facilitate statistical modeling of complex distributions of hydrologic properties in the subsurface. The results produced by the simulation reveal high accuracy using the robust algorithm to optimize three dimensional FDTD forward modeling of GPR responses in heterogeneous surroundings.

  6. Diesel engine emissions and combustion predictions using advanced mixing models applicable to fuel sprays

    NASA Astrophysics Data System (ADS)

    Abani, Neerav; Reitz, Rolf D.

    2010-09-01

    An advanced mixing model was applied to study engine emissions and combustion with different injection strategies ranging from multiple injections, early injection and grouped-hole nozzle injection in light and heavy duty diesel engines. The model was implemented in the KIVA-CHEMKIN engine combustion code and simulations were conducted at different mesh resolutions. The model was compared with the standard KIVA spray model that uses the Lagrangian-Drop and Eulerian-Fluid (LDEF) approach, and a Gas Jet spray model that improves predictions of liquid sprays. A Vapor Particle Method (VPM) is introduced that accounts for sub-grid scale mixing of fuel vapor and more accurately and predicts the mixing of fuel-vapor over a range of mesh resolutions. The fuel vapor is transported as particles until a certain distance from nozzle is reached where the local jet half-width is adequately resolved by the local mesh scale. Within this distance the vapor particle is transported while releasing fuel vapor locally, as determined by a weighting factor. The VPM model more accurately predicts fuel-vapor penetrations for early cycle injections and flame lift-off lengths for late cycle injections. Engine combustion computations show that as compared to the standard KIVA and Gas Jet spray models, the VPM spray model improves predictions of in-cylinder pressure, heat released rate and engine emissions of NOx, CO and soot with coarse mesh resolutions. The VPM spray model is thus a good tool for efficiently investigating diesel engine combustion with practical mesh resolutions, thereby saving computer time.

  7. An open framework for hydrological data assimilation

    NASA Astrophysics Data System (ADS)

    Madsen, H.; Ridler, M. E.; Velzen, N. V.; Hummel, S.; Sandholt, I.; Falk, A. K.; Heemink, A.

    2013-12-01

    Accurate and reliable real-time hydrological forecasts are essential for protection against water-related hazards, operation of infrastructure, and water resources management. Recent advances in radar rainfall estimation and forecasting, numerical weather predictions, satellite and in-situ monitoring, and faster computing facilities are opening up new opportunities in real-time hydrological forecasting. More effective use of the different information sources via data assimilation will provide the basis for producing more accurate and more reliable forecasts. In this regard, development and implementation of robust and computationally efficient data assimilation algorithms that are feasible for real-time applications remains one of the key challenges. Thus far, many of the efforts on implementation of data assimilation in hydrological modeling have been model specific. This requires access to as well as an in-depth knowledge of the numerical core of the models. A means to deal with the interaction between model and data assimilation algorithm in a more generic way is the use of the Open Model Interface (OpenMI). This open source standard interface allows models to exchange data during runtime, thus transforming a complex numerical model to a plug and play like component. For data assimilation, the OpenDA data assimilation toolbox is an open interface standard that includes a set of tools, assimilation algorithms, and numerical techniques to quickly implement data assimilation in numerical models. This paper presents a new generic data assimilation framework that uses OpenMI to interface models within OpenDA. This enables the many OpenMI hydrological models already available access to a robust and flexible data assimilation library. A synthetic test case is presented that highlights the potential of this new framework. An ensemble based Kalman filter is demonstrated for assimilation of groundwater levels in the MIKE SHE distributed and integrated hydrological

  8. Watershed hydrology, network allometry and ecosystem structure

    NASA Astrophysics Data System (ADS)

    Rinaldo, A.

    2003-04-01

    The lecture covers recent advances relevant to watershed hydrology, in particular derived from the realm of data now available, covering a wide range of scales and objectively collected and analyzed. It is intended to summarize results that are, in the lecturer's opinion, crucial to our current understanding of a variety of issues. Key among them, landscape evolution models, models of the hydrologic response and, indeed a scientific challenge, ecosystem structure. In particular, a new allometric scaling law for loopless networks, confirmed through studies on rivers, exact network results and computer simulations, offers unique insight on a variety of phenomena, ranging from the ubiquity of the 'quarter-power' law in biology to the origin of scaling size spectra in marine microbial ecosystems, to the proper geomorphological description of a river basin and its hydrological implications. In a sense, networks are a byproduct of the hydrologic dynamics, and indeed can be shown to be related to ecosystem structure. Si parva licet, I will provide evidence suggesting that ensemble averaging of the allometric property (where individual realizations are different networks) leads to results in excellent accord with the known limit scaling of efficient and compact networks with remarkably little scatter with implications of somewhat general character. Such results complement recent work suggesting that scaling features are quite robust to geometrical fluctuations of network properties. Finally, I shall gather from the morphological analysis on river networks the potential for predicting the main characters of the hydrologic response in ungauged basins - a task of practical nature with many social implications, possibly relevant to the Session's aims.

  9. Predictions for Swift Follow-up Observations of Advanced LIGO/Virgo Gravitational Wave Sources

    NASA Astrophysics Data System (ADS)

    Racusin, Judith; Evans, Phil; Connaughton, Valerie

    2015-04-01

    The likely detection of gravitational waves associated with the inspiral of neutron star binaries by the upcoming advanced LIGO/Virgo observatories will be complemented by searches for electromagnetic counterparts over large areas of the sky by Swift and other observatories. As short gamma-ray bursts (GRB) are the most likely electromagnetic counterpart candidates to these sources, we can make predictions based upon the last decade of GRB observations by Swift and Fermi. Swift is uniquely capable of accurately localizing new transients rapidly over large areas of the sky in single and tiled pointings, enabling ground-based follow-up. We describe simulations of the detectability of short GRB afterglows by Swift given existing and hypothetical tiling schemes with realistic observing conditions and delays, which guide the optimal observing strategy and improvements provided by coincident detection with observatories such as Fermi-GBM.

  10. Edge Fracture Prediction ofTraditional and Advanced Trimming Processes for AA6111-T4 Sheets

    SciTech Connect

    Hu, Xiaohua; Choi, Kyoo Sil; Sun, Xin; Golovashchenko, Segey F.

    2014-02-15

    This work examines the traditional and advanced trimming of AA6111-T4 aluminum sheets with finite element simulations. The Rice-Tracy damage model is used for the simulation with damage parameters estimated from experimental observation of grain aspect ratio near the fracture surface of trimmed parts. Fine meshes at the shearing zone, adaptive meshing, and adaptive contact techniques are used to accurately capture the contact interactions between the sharp corner of the trimming tools and the blank to be trimmed. To the knowledge of the authors, these are the first trimming simulations that can predict the effects of shearing clearance on burr heights with quantitative accuracy for AA6111-T4 aluminum sheets. In addition, the models have also accurately reproduced the crack initiation site as well as burr and sliver formation mechanisms observed experimentally.

  11. Regional Arctic System Model (RASM): A Tool to Address the U.S. Priorities and Advance Capabilities for Arctic Climate Modeling and Prediction

    NASA Astrophysics Data System (ADS)

    Maslowski, W.; Roberts, A.; Cassano, J. J.; Gutowski, W. J., Jr.; Nijssen, B.; Osinski, R.; Zeng, X.; Brunke, M.; Duvivier, A.; Hamman, J.; Hossainzadeh, S.; Hughes, M.; Seefeldt, M. W.

    2015-12-01

    The Arctic is undergoing some of the most coordinated rapid climatic changes currently occurring anywhere on Earth, including the retreat of the perennial sea ice cover, which integrates forcing by, exchanges with and feedbacks between atmosphere, ocean and land. While historical reconstructions from Earth System Models (ESMs) are in broad agreement with these changes, the rate of change in ESMs generally remains outpaced by observations. Reasons for that relate to a combination of coarse resolution, inadequate parameterizations, under-represented processes and a limited knowledge of physical interactions. We demonstrate the capability of the Regional Arctic System Model (RASM) in addressing some of the ESM limitations in simulating observed variability and trends in arctic surface climate. RASM is a high resolution, pan-Arctic coupled climate model with the sea ice and ocean model components configured at an eddy-permitting resolution of 1/12o and the atmosphere and land hydrology model components at 50 km resolution, which are all coupled at 20-minute intervals. RASM is an example of limited-area, process-resolving, fully coupled ESM, which due to the constraints from boundary conditions facilitates detailed comparisons with observational statistics that are not possible with ESMs. The overall goal of RASM is to address key requirements published in the Navy Arctic Roadmap: 2014-2030 and in the Implementation Plan for the National Strategy for the Arctic Region, regarding the need for advanced modeling capabilities for operational forecasting and strategic climate predictions through 2030. The main science objectives of RASM are to advance understanding and model representation of critical physical processes and feedbacks of importance to sea ice thickness and area distribution. RASM results are presented to quantify relative contributions by (i) resolved processes and feedbacks as well as (ii) sensitivity to space dependent sub-grid parameterizations to better

  12. Surprise Questions for Survival Prediction in Patients With Advanced Cancer: A Multicenter Prospective Cohort Study

    PubMed Central

    Hamano, Jun; Morita, Tatsuya; Inoue, Satoshi; Ikenaga, Masayuki; Matsumoto, Yoshihisa; Sekine, Ryuichi; Yamaguchi, Takashi; Hirohashi, Takeshi; Tajima, Tsukasa; Tatara, Ryohei; Watanabe, Hiroaki; Otani, Hiroyuki; Takigawa, Chizuko; Matsuda, Yoshinobu; Nagaoka, Hiroka; Mori, Masanori; Yamamoto, Naoki; Shimizu, Mie; Sasara, Takeshi

    2015-01-01

    Background. Predicting the short-term survival in cancer patients is an important issue for patients, family, and oncologists. Although the prognostic accuracy of the surprise question has value in 1-year mortality for cancer patients, the prognostic value for short-term survival has not been formally assessed. The primary aim of the present study was to assess the prognostic value of the surprise question for 7-day and 30-day survival in patients with advanced cancer. Patients and Methods. The present multicenter prospective cohort study was conducted in Japan from September 2012 through April 2014, involving 16 palliative care units, 19 hospital-based palliative care teams, and 23 home-based palliative care services. Results. We recruited 2,425 patients and included 2,361 for analysis: 912 from hospital-based palliative care teams, 895 from hospital palliative care units, and 554 from home-based palliative care services. The sensitivity, specificity, positive predictive value, and negative predictive value of the 7-day survival surprise question were 84.7% (95% confidence interval [CI], 80.7%–88.0%), 68.0% (95% CI, 67.3%–68.5%), 30.3% (95% CI, 28.9%–31.5%), and 96.4% (95% CI, 95.5%–97.2%), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for the 30-day surprise question were 95.6% (95% CI, 94.4%–96.6%), 37.0% (95% CI, 35.9%–37.9%), 57.6% (95% CI, 56.8%–58.2%), and 90.4% (95% CI, 87.7%–92.6%), respectively. Conclusion. Surprise questions are useful for screening patients for short survival. However, the high false-positive rates do not allow clinicians to provide definitive prognosis prediction. Implications for Practice: The findings of this study indicate that clinicians can screen patients for 7- or 30-day survival using surprise questions with 90% or more sensitivity. Clinicians cannot provide accurate prognosis estimation, and all patients will not always die within the defined periods. The

  13. An Advanced Data Assimilation System for Estuary and Coastal Ocean Prediction

    NASA Astrophysics Data System (ADS)

    Hoffman, M. J.; Murtugudde, R.; Brown, C. W.

    2008-12-01

    We are developing an advanced data assimilation system for the Chesapeake Bay Forecast System, a regional Earth System Prediction model. To accomplish this, the Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS) has been interfaced with the Local Ensemble Transform Kalman Filter (LETKF) to create an efficient data assimilation system. The LETKF, an ensemble Kalman filtering scheme developed at the University of Maryland, is among the most advanced data assimilation methods and is very effective for large, non-linear dynamical systems in both sparse and dense data coverage situations. Crucial to the LETKF-ChesROMS assimilation system is having accurate open ocean boundary conditions from GODAE and other large scale products. Currently, the assimilation system is run with prescribed climatological boundary conditions in a relatively coarse resolution. In perfect model experiments using ChesROMS, the filter converges quickly and greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and velocity fields. This error reduction has proved fairly robust to sensitivity studies such as reduced data coverage. The LETKF also provides an efficient algorithm for error estimation and facilitates the investigation of the spatial distribution of the error. This information will be used to determine areas where more monitoring is needed and to address other issues of the observational impacts on the analyses and observational system simulation experiments, in addition to forecast initialization experiments and regional reanalyses for the past decade.

  14. ISOTOPE HYDROLOGY LABORATORY (WATER QUALITY MANAGEMENT BRANCH, WATER SUPPLY AND WATER RESOURCES DIVISION, NRMRL)

    EPA Science Inventory

    The mission of NRMRL's Water Supply and Water Resources Division's Isotope Hydrology Laboratory (IHL) is to resolve environmental hydrology problems through research and application of naturally occurring isotopes.The emergent field of isotope hydrology follows advances in anal...

  15. Can functional gene abundance predict N-fluxes? Examples from a well-studied hydrological flow path in a forested watershed in SW China

    NASA Astrophysics Data System (ADS)

    Liu, Binbin; Muzammil, Bushra; Dörsch, Peter; Zhu, Jing; Mulder, Jan; Frostegård, Åsa

    2014-05-01

    Edaphic, climatic and management factors shape soil microbial communities taxonomically and functionally, resulting in spatial separation of nitrogen (N) oxidation and reduction processes along hydrological flowpaths. In a recent study, we investigated N-cycling processes and N2O emissions along a mesic hillslope (HS) and a hydrologically connected groundwater discharge zone (GDZ) in a forested headwater catchment dominated by acid soils (pH 4.0 - 4.5) in subtropical China (Chongqing). The watershed receives 50 kg N ha-1 a-1 through atmogenic deposition (2/3 as ammonium), most of which is removed before discharge. Surprisingly, N2O emissions were found to be greatest on the well-drained HS, whereas a drop of NO3- concentrations along the flow path indicated that N removal was highest in the moist GDZ. Nitrification was assumed to be none-limiting as the total flux of NO3- leaving the hill slope soils roughly equalled the input of NH4+. To understand watershed N-cycling and removal in more detail, we studied the abundance of functional genes involved in ammonium oxidation (amoA of AOB and AOA), nitrite oxidation (nxrB) and denitrification (nirK, nirS, nosZ) in top soils from 8 locations along the flow path spanning from the hilltop to the outlet of the GDZ. 16S rRNA gene abundance was assessed as a general marker for bacterial abundance. All genes showed highest abundance per gram soil in the heavily disturbed GDZ (formerly cultivated terraces), despite lower soil organic carbon content (1-4% w/w as opposed to 10-20% w/w in HS topsoil) and periodically stagnant conditions due to high water tables after monsoonal rainfalls. Ratios of nosZ/nirS+nirK, commonly used to predict denitrification product stoichiometry (N2O/N2), yielded counterintuitive results with higher values for HS than for GDZ. However, comparing nir gene with 16S rRNA gene abundance revealed that denitrifiers accounted for up to 10% of the bacterial community in the GDZ soils whereas this value was

  16. Investigation to advance prediction techniques of the low-speed aerodynamics of V/STOL aircraft

    NASA Technical Reports Server (NTRS)

    Maskew, B.; Strash, D.; Nathman, J.; Dvorak, F. A.

    1985-01-01

    A computer program, VSAERO, has been applied to a number of V/STOL configurations with a view to advancing prediction techniques for the low-speed aerodynamic characteristics. The program couples a low-order panel method with surface streamline calculation and integral boundary layer procedures. The panel method--which uses piecewise constant source and doublet panels-includes an iterative procedure for wake shape and models boundary layer displacement effect using the source transpiration technique. Certain improvements to a basic vortex tube jet model were installed in the code prior to evaluation. Very promising results were obtained for surface pressures near a jet issuing at 90 deg from a flat plate. A solid core model was used in the initial part of the jet with a simple entrainment model. Preliminary representation of the downstream separation zone significantly improve the correlation. The program accurately predicted the pressure distribution inside the inlet on the Grumman 698-411 design at a range of flight conditions. Furthermore, coupled viscous/potential flow calculations gave very close correlation with experimentally determined operational boundaries dictated by the onset of separation inside the inlet. Experimentally observed degradation of these operational boundaries between nacelle-alone tests and tests on the full configuration were also indicated by the calculation. Application of the program to the General Dynamics STOL fighter design were equally encouraging. Very close agreement was observed between experiment and calculation for the effects of power on pressure distribution, lift and lift curve slope.

  17. [Plasma Biomarkers as Predictive Factors for Advanced Hepatocellular Carcinoma with Sorafenib].

    PubMed

    Shiozawa, Kazue; Watanabe, Manabu; Ikehara, Takashi; Matsukiyo, Yasushi; Kogame, Michio; Shinohara, Mie; Kikuchi, Yoshinori; Igarashi, Yoshinori; Sumino, Yasukiyo

    2016-07-01

    We examined plasma biomarkers as predictive factors for advanced hepatocellular carcinoma(ad-HCC)patients treated with sorafenib. We analyzed a-fetoprotein(AFP), AFP-L3, des-g-carboxy prothrombin(DCP), neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), and vascular endothelial growth factor(VEGF)before sorafenib therapy, and changes in AFP-L3, NLR, PLR, and VEGF 1 month after sorafenib therapy in 16 patients. High AFP-L3(hazard ratio: 1.058, 95%CI: 1.019-1.098, p=0.003)and high NLR(hazard ratio: 1.475, 95%CI: 1.045-2.082, p=0.027)were significantly associated with poor prognosis in ad-HCC patients treated with sorafenib. There were no significant differences in changes in AFP-L3, NLR, PLR, and VEGF 1 month after sorafenib therapy. We suggest that AFP-L3 and NLR levels before sorafenib therapy in patients with ad-HCC are an important predictive factor for the therapeutic effect of sorafenib and patient survival. PMID:27431630

  18. Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

    NASA Astrophysics Data System (ADS)

    Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

    2015-09-01

    To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

  19. Satellite-derived potential evapotranspiration for distributed hydrologic runoff modeling

    NASA Astrophysics Data System (ADS)

    Spies, R. R.; Franz, K. J.; Bowman, A.; Hogue, T. S.; Kim, J.

    2012-12-01

    Distributed models have the ability of incorporating spatially variable data, especially high resolution forcing inputs such as precipitation, temperature and evapotranspiration in hydrologic modeling. Use of distributed hydrologic models for operational streamflow prediction has been partially hindered by a lack of readily available, spatially explicit input observations. Potential evapotranspiration (PET), for example, is currently accounted for through PET input grids that are based on monthly climatological values. The goal of this study is to assess the use of satellite-based PET estimates that represent the temporal and spatial variability, as input to the National Weather Service (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). Daily PET grids are generated for six watersheds in the upper Mississippi River basin using a method that applies only MODIS satellite-based observations and the Priestly Taylor formula (MODIS-PET). The use of MODIS-PET grids will be tested against the use of the current climatological PET grids for simulating basin discharge. Gridded surface temperature forcing data are derived by applying the inverse distance weighting spatial prediction method to point-based station observations from the Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS). Precipitation data are obtained from the Climate Prediction Center's (CPC) Climatology-Calibrated Precipitation Analysis (CCPA). A-priori gridded parameters for the Sacramento Soil Moisture Accounting Model (SAC-SMA), Snow-17 model, and routing model are initially obtained from the Office of Hydrologic Development and further calibrated using an automated approach. The potential of the MODIS-PET to be used in an operational distributed modeling system will be assessed with the long-term goal of promoting research to operations transfers and advancing the science of hydrologic forecasting.

  20. CUAHSI Hydrologic Information System and its role in hydrologic observatories

    NASA Astrophysics Data System (ADS)

    Maidment, D.; Helly, J. J.; Graham, W.; Kruger, A.; Kumar, P.; Lakshmi, V.; Lettenmaier, D.; Zheng, C.; Lall, U.; Piasecki, M.; Duffy, C.

    2003-12-01

    The Hydrologic Information System component of CUAHSI focuses on building a hydrologic information system to support the advancement of hydrologic science. This system is intended to help with rapidly acquiring diverse geospatial and temporal hydrologic datasets, integrating them into a hydrologic data model or framework describing a region, and supporting analysis, modeling and visualization of the movement of water and the transport of constituents through that region. In addition, the system will feature interfaces for advanced technologies like knowledge discovery in databases (KDD) and also provide a comprehensive metadata description including a hydrologic ontology (HOW) for integration with the Semantic Web. The prototype region is the Neuse river basin in North Carolina. A "digital watershed" is to be built for this basin to help formulate and test the hydrologic data model at a range of spatial scales, from the scale of the whole basin down to the scale of individual experimental sites. This data model will be further developed and refined as additional hydrologic observatories are selected by CUAHSI. This will result in a consistent means for the characterization and comparison of processes in different geographic regions of the nation using a common data framework. The HIS will also provide a generalized digital library capability to manage collections of thematically-organized data from primary sources as well as derived analytical results in the form of data publications. The HIS will be designed from the beginning as an open federation of observatory-based collections that are interoperable with other data and digital library systems. The CUAHSI Hydrologic Information System project involves collaboration among several CUAHSI member institutions, with the San Diego Supercomputer Center serving as the technology partner to facilitate the development of a prototype system.

  1. Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

    NASA Astrophysics Data System (ADS)

    Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

    2010-12-01

    Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on

  2. Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

    NASA Astrophysics Data System (ADS)

    Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

    2015-06-01

    Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn-Broken catchment, Australia, for the "Millennium Drought" (1997-2009) relative to the period 1983-1995, and for two future periods (2021-2050 and 2071-2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981-2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7-66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3-10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021-2050) and 2.3 to 23.1 % later in the century (2071-2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5-25.9 % and end of century 2.6-24.2 %) were found for climate change under the RCP8.5 emission scenario

  3. The Platte River Hydrologic Observatory (PRIVHO)

    NASA Astrophysics Data System (ADS)

    Harvey, F.; Ramirez, J. A.; Thurow, T. L.

    2004-12-01

    The Platte River Hydrologic Observatory (PRIVHO), located within the Platte River Basin, of the U.S. central Great Plains, affords excellent interdisciplinary and multi-disciplinary research opportunities for scientists to examine the impacts of scaling, to investigate forcing feedbacks and coupling of various interconnected hydrological, geological, climatological and biological systems, and to test the applicability and limits of prediction in keeping with all five of CUAHSI's priority science criteria; linking hydrologic and biogeochemical cycles, sustainability of water resources, hydrologic and ecosystem interactions, hydrologic extremes, and fate and transport of contaminants. In addition, PRIVHO is uniquely positioned to investigate many human dimension questions such as those related to interstate and intrastate conflicts over water use, evolution of water policy and law in the wake of advancing science, societal and economic changes that are driven by water use, availability and management, and human impacts on climate and land use changes. The Platte River traverses several important environmental gradients, including temperature and precipitation-to-evaporation ratio, is underlain by the High Plains Aquifer under much of its reach, crosses a number of terrestrial ecoregions, and in central Nebraska, serves as a vital link in the Central Flyway, providing habitat for 300 species of migratory birds and many threatened or endangered species. The Platte River flows through metropolitan, urban and agricultural settings and is impacted by both point and non-point pollution. The Platte River is one of the most over-appropriated rivers in the country with 15 major dams, hundreds of small reservoirs, and thousands of irrigation wells. The river provides municipal and industrial water supplies for about 3.5 million people, irrigation water for millions of acres of farmland, and generates millions of dollars of hydroelectric power. PRIVHO will allow researchers to

  4. 15-PGDH expression as a predictive factor response to neoadjuvant chemotherapy in advanced gastric cancer

    PubMed Central

    Hu, Min; Li, Kai; Maskey, Ninu; Xu, Zhigao; Peng, ChunWei; Tian, Sufang; Li, Yan; Yang, Guifang

    2015-01-01

    Given the various clinical and pathologic responses to neoadjuvant chemotherapy (NACT) in gastric cancer (GC), potential biomarkers that reflecting the efficacy of NACT on GC should be investigated. The aim of this study was to investigate the 15-PGDH expression response to NACT in GC patients and its relationship with prognosis of GC. Immunohistochemical method was used to assess the level of 15-PGDH expression in 56 GC patients who received NACT before surgery and 46 patients who underwent surgical treatment without NACT as well as their corresponding adjacent non-neoplastic tissues. We found that there was no correlation of 15-PGDH expression between non-cancerous gastric tissues and GC tissues (P=0.519), while 15-PGDH expression level in NACT group was higher than that in nNACT group (P=0.015). In patients with NACT, the higher level of 15-PGDH expression was significantly associated with well-moderately differentiated grade (P=0.023), I/II stage (P=0.014) and with no lymph node metastasis (P=0.016). Moreover, statistically significant differences in overall survival (OS) were found among 15-PGDH expression (log-rank test, P<0.001) and TNM stage (log-rank test, P=0.032). Most importantly, expression of 15-PGDH was found to be an independent predictive factor by multivariate analysis (Hazard ratio (HR) 0.315 [0.120-0.827], P=0.019). These findings indicated that NACT could increase 15-PGDH expression in advanced GC patients, and 15-PGDH may serve as a candidate prognostic biomarker of advanced GC response to NACT. PMID:26261578

  5. Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

    PubMed

    Kourgialas, Nektarios N; Dokou, Zoi; Karatzas, George P

    2015-05-01

    The purpose of this study was to create a modeling management tool for the simulation of extreme flow events under current and future climatic conditions. This tool is a combination of different components and can be applied in complex hydrogeological river basins, where frequent flood and drought phenomena occur. The first component is the statistical analysis of the available hydro-meteorological data. Specifically, principal components analysis was performed in order to quantify the importance of the hydro-meteorological parameters that affect the generation of extreme events. The second component is a prediction-forecasting artificial neural network (ANN) model that simulates, accurately and efficiently, river flow on an hourly basis. This model is based on a methodology that attempts to resolve a very difficult problem related to the accurate estimation of extreme flows. For this purpose, the available measurements (5 years of hourly data) were divided in two subsets: one for the dry and one for the wet periods of the hydrological year. This way, two ANNs were created, trained, tested and validated for a complex Mediterranean river basin in Crete, Greece. As part of the second management component a statistical downscaling tool was used for the creation of meteorological data according to the higher and lower emission climate change scenarios A2 and B1. These data are used as input in the ANN for the forecasting of river flow for the next two decades. The final component is the application of a meteorological index on the measured and forecasted precipitation and flow data, in order to assess the severity and duration of extreme events. PMID:25710600

  6. The role of advanced reactive surface area characterization in improving predictions of mineral reaction rates

    NASA Astrophysics Data System (ADS)

    Beckingham, L. E.; Zhang, S.; Mitnick, E.; Cole, D. R.; Yang, L.; Anovitz, L. M.; Sheets, J.; Swift, A.; Kneafsey, T. J.; Landrot, G.; Mito, S.; Xue, Z.; Steefel, C. I.; DePaolo, D. J.; Ajo Franklin, J. B.

    2014-12-01

    Geologic sequestration of CO2 in deep sedimentary formations is a promising means of mitigating carbon emissions from coal-fired power plants but the long-term fate of injected CO2 is challenging to predict. Reactive transport models are used to gain insight over long times but rely on laboratory determined mineral reaction rates that have been difficult to extrapolate to field systems. This, in part, is due to a lack of understanding of mineral reactive surface area. Many models use an arbitrary approximation of reactive surface area, applying orders of magnitude scaling factors to measured BET or geometric surface areas. Recently, a few more sophisticated approaches have used 2D and 3D image analyses to determine mineral-specific reactive surface areas that account for the accessibility of minerals. However, the ability of these advanced surface area estimates to improve predictions of mineral reaction rates has yet to be determined. In this study, we fuse X-ray microCT, SEM QEMSCAN, XRD, SANS, and SEM-FIB analysis to determine mineral-specific accessible reactive surface areas for a core sample from the Nagaoka pilot CO2 injection site (Japan). This sample is primarily quartz, plagioclase, smectite, K-feldspar, and pyroxene. SEM imaging shows abundant smectite cement and grain coatings that decrease the fluid accessibility of other minerals. However, analysis of FIB-SEM images reveals that smectite nano-pores are well connected such that access to underlying minerals is not occluded by smectite coatings. Mineral-specific accessible surfaces are determined, accounting for the connectivity of the pore space with and without connected smectite nano-pores. The large-scale impact of variations in accessibility and dissolution rates are then determined through continuum scale modeling using grid-cell specific information on accessible surface areas. This approach will be compared with a traditional continuum scale model using mineral abundances and common surface area

  7. Arctic hydrology and meteorology

    SciTech Connect

    Kane, D.L.

    1989-01-01

    To date, five years of hydrologic and meteorologic data have been collected at Imnavait Creek near Toolik Lake, Alaska. This is the most complete set of field data of this type collected in the Arctic of North America. These data have been used in process-oriented research to increase our understanding of atmosphere/hydrosphere/biosphere/lithosphere interactions. Basically, we are monitoring heat and mass transfer between various spheres to quantify rates. These could be rates of mass movement such as hillslope flow or rates of heat transfer for active layer thawing or combined heat and mass processes such as evapotranspiration. We have utilized a conceptual model to predict hydrologic processes. To test the success of this model, we are comparing our predicted rates of runoff and snowmelt to measured valves. We have also used a surface energy model to simulate active layer temperatures. The final step in this modeling effort to date was to predict what impact climatic warming would have on active layer thicknesses and how this will influence the hydrology of our research watershed by examining several streambeds.

  8. In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation

    SciTech Connect

    G. R. Odette; G. E. Lucas

    2005-11-15

    This final report on "In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation" (DE-FG03-01ER54632) consists of a series of summaries of work that has been published, or presented at meetings, or both. It briefly describes results on the following topics: 1) A Transport and Fate Model for Helium and Helium Management; 2) Atomistic Studies of Point Defect Energetics, Dynamics and Interactions; 3) Multiscale Modeling of Fracture consisting of: 3a) A Micromechanical Model of the Master Curve (MC) Universal Fracture Toughness-Temperature Curve Relation, KJc(T - To), 3b) An Embrittlement DTo Prediction Model for the Irradiation Hardening Dominated Regime, 3c) Non-hardening Irradiation Assisted Thermal and Helium Embrittlement of 8Cr Tempered Martensitic Steels: Compilation and Analysis of Existing Data, 3d) A Model for the KJc(T) of a High Strength NFA MA957, 3e) Cracked Body Size and Geometry Effects of Measured and Effective Fracture Toughness-Model Based MC and To Evaluations of F82H and Eurofer 97, 3-f) Size and Geometry Effects on the Effective Toughness of Cracked Fusion Structures; 4) Modeling the Multiscale Mechanics of Flow Localization-Ductility Loss in Irradiation Damaged BCC Alloys; and 5) A Universal Relation Between Indentation Hardness and True Stress-Strain Constitutive Behavior. Further details can be found in the cited references or presentations that generally can be accessed on the internet, or provided upon request to the authors. Finally, it is noted that this effort was integrated with our base program in fusion materials, also funded by the DOE OFES.

  9. Impact of Uncertainty Characterization of Satellite Rainfall Inputs and Model Parameters on Hydrological Data Assimilation with the Ensemble Kalman Filter for Flood Prediction

    NASA Astrophysics Data System (ADS)

    Vergara, H. J.; Kirstetter, P.; Hong, Y.; Gourley, J. J.; Wang, X.

    2013-12-01

    The Ensemble Kalman Filter (EnKF) is arguably the assimilation approach that has found the widest application in hydrologic modeling. Its relatively easy implementation and computational efficiency makes it an attractive method for research and operational purposes. However, the scientific literature featuring this approach lacks guidance on how the errors in the forecast need to be characterized so as to get the required corrections from the assimilation process. Moreover, several studies have indicated that the performance of the EnKF is 'sub-optimal' when assimilating certain hydrologic observations. Likewise, some authors have suggested that the underlying assumptions of the Kalman Filter and its dependence on linear dynamics make the EnKF unsuitable for hydrologic modeling. Such assertions are often based on ineffectiveness and poor robustness of EnKF implementations resulting from restrictive specification of error characteristics and the absence of a-priori information of error magnitudes. Therefore, understanding the capabilities and limitations of the EnKF to improve hydrologic forecasts require studying its sensitivity to the manner in which errors in the hydrologic modeling system are represented through ensembles. This study presents a methodology that explores various uncertainty representation configurations to characterize the errors in the hydrologic forecasts in a data assimilation context. The uncertainty in rainfall inputs is represented through a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which provides information about second-order statistics of quantitative precipitation estimates (QPE) error. The uncertainty in model parameters is described adding perturbations based on parameters covariance information. The method allows for the identification of rainfall and parameter perturbation combinations for which the performance of the EnKF is 'optimal' given a set of objective functions. In this process, information about

  10. Arctic hydrology and meteorology

    SciTech Connect

    Kane, D.L.

    1988-01-01

    The behavior of arctic ecosystems is directly related to the ongoing physical processes of heat and mass transfer. Furthermore, this system undergoes very large fluctuations in the surface energy balance. The buffering effect of both snow and the surface organic soils can be seen by looking at the surface and 40 cm soil temperatures. The active layer, that surface zone above the permafrost table, is either continually freezing or thawing. A large percentage of energy into and out of a watershed must pass through this thin veneer that we call the active layer. Likewise, most water entering and leaving the watershed does so through the active layer. To date, we have been very successful at monitoring the hydrology of Imnavait Creek with special emphasis on the active layer processes. The major contribution of this study is that year-round hydrologic data are being collected. An original objective of our study was to define how the thermal and moisture regimes within the active layer change during an annual cycle under natural conditions, and then to define how the regime will be impacted by some imposed terrain alteration. Our major analysis of the hydrologic data sets for Imnavait Creek have been water balance evaluations for plots during snowmelt, water balance for the watershed during both rainfall and snowmelt, and the application of a hydrologic model to predict the Imnavait Creek runoff events generated by both snowmelt and rainfall.

  11. Predictive value of serum medroxyprogesterone acetate concentration for response in advanced or recurrent breast cancer.

    PubMed

    Nishimura, R; Nagao, K; Matsuda, M; Baba, K; Matsuoka, Y; Yamashita, H; Fukuda, M; Higuchi, A; Ikeda, K

    1997-08-01

    Medroxyprogesterone acetate (MPA) is one of the most commonly prescribed drugs for endocrine therapy of metastatic breast cancer. In this study, the serum MPA concentration was measured by high-performance liquid chromatography (HPLC) and evaluated for its usefulness in predicting the response in 79 cases of advanced or recurrent breast cancers. Overall, 29 patients (37%) achieved an objective response. The response rate correlated significantly with the oestrogen receptor (ER) status (P = 0.03), proliferative activity determined by DNA polymerase alpha (P = 0.04), the disease-free interval (DFI) (P = 0.05) and the serum MPA concentration (P < 0.001). Patients with ER-positive tumours, lower proliferative activity, a longer (DFI) or a higher serum MPA concentration responded more frequently. The mean serum MPA concentration in the responders with ER-positive tumours (P = 0.01) or tumours with a lower proliferative activity (P = 0.008) were significantly lower than in cases with ER-negative tumours or tumours with a higher proliferative activity, respectively. Cases with soft tissue metastases showed responses at significantly lower MPA concentrations (P = 0.003) than those with bone or visceral metastases. Furthermore, there was a dramatic decrease in the MPA concentration when a responder with a high concentration became unresponsive to the therapy. Thus, the serum MPA concentration is a determining factor for the response to treatment. PMID:9337682

  12. Methodological advances in predicting flow-induced dynamics of plants using mechanical-engineering theory.

    PubMed

    de Langre, Emmanuel

    2012-03-15

    The modeling of fluid-structure interactions, such as flow-induced vibrations, is a well-developed field of mechanical engineering. Many methods exist, and it seems natural to apply them to model the behavior of plants, and potentially other cantilever-like biological structures, under flow. Overcoming this disciplinary divide, and the application of such models to biological systems, will significantly advance our understanding of ecological patterns and processes and improve our predictive capabilities. Nonetheless, several methodological issues must first be addressed, which I describe here using two practical examples that have strong similarities: one from agricultural sciences and the other from nuclear engineering. Very similar issues arise in both: individual and collective behavior, small and large space and time scales, porous modeling, standard and extreme events, trade-off between the surface of exchange and individual or collective risk of damage, variability, hostile environments and, in some aspects, evolution. The conclusion is that, although similar issues do exist, which need to be exploited in some detail, there is a significant gap that requires new developments. It is obvious that living plants grow in and adapt to their environment, which certainly makes plant biomechanics fundamentally distinct from classical mechanical engineering. Moreover, the selection processes in biology and in human engineering are truly different, making the issue of safety different as well. A thorough understanding of these similarities and differences is needed to work efficiently in the application of a mechanistic approach to ecology. PMID:22357585

  13. A Priori Attitudes Predict Amniocentesis Uptake in Women of Advanced Maternal Age: A Pilot Study.

    PubMed

    Grinshpun-Cohen, Julia; Miron-Shatz, Talya; Rhee-Morris, Laila; Briscoe, Barbara; Pras, Elon; Towner, Dena

    2015-01-01

    Amniocentesis is an invasive procedure performed during pregnancy to determine, among other things, whether the fetus has Down syndrome. It is often preceded by screening, which gives a probabilistic risk assessment. Thus, ample information is conveyed to women with the goal to inform their decisions. This study examined the factors that predict amniocentesis uptake among pregnant women of advanced maternal age (older than 35 years old at the time of childbirth). Participants filled out a questionnaire regarding risk estimates, demographics, and attitudes on screening and pregnancy termination before their first genetic counseling appointment and were followed up to 24 weeks of gestation. Findings show that women's decisions are not always informed by screening results or having a medical indication. Psychological factors measured at the beginning of pregnancy: amniocentesis risk tolerance, pregnancy termination tolerance, and age risk perception affected amniocentesis uptake. Although most women thought that screening for Down syndrome risk would inform their decision, they later stated other reasons for screening, such as preparing for the possibility of a child with special needs. Findings suggest that women's decisions regarding amniocentesis are driven not only by medical factors, but also by a priori attitudes. The authors believe that these should be addressed in the dialogue on women's informed use of prenatal tests. PMID:26065331

  14. APPLICATION OF ADVANCED IN VITRO TECHNIQUES TO MEASURE, UNDERSTAND AND PREDICT THE KINETICS AND MECHANISMS OF XENOBIOTIC METABOLISM

    EPA Science Inventory

    We have developed a research program in metabolism that involves numerous collaborators across EPA as well as other federal and academic labs. A primary goal is to develop and apply advanced in vitro techniques to measure, understand and predict the kinetics and mechanisms of xen...

  15. A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

  16. Development of Computational Capabilities to Predict the Corrosion Wastage of Boiler Tubes in Advanced Combustion Systems

    SciTech Connect

    Kung, Steven; Rapp, Robert

    2014-08-31

    coal-fired boilers resulting from the coexistence of sulfur and chlorine in the fuel. A new corrosion mechanism, i.e., “Active Sulfidation Corrosion Mechanism,” has been proposed to account for the accelerated corrosion wastage observed on the furnace walls of utility boilers burning coals containing sulfur and chlorine. In addition, a second corrosion mechanism, i.e., “Active Sulfide-to-Oxide Corrosion Mechanism,” has been identified to account for the rapid corrosion attack on superheaters and reheaters. Both of the newly discovered corrosion mechanisms involve the formation of iron chloride (FeCl2) vapor from iron sulfide (FeS) and HCl, followed by the decomposition of FeCl2 via self-sustaining cycling reactions. For higher alloys containing sufficient chromium, the attack on superheaters and reheaters is dominated by Hot Corrosion in the presence of a fused salt. Furthermore, two stages of the hot corrosion mechanism have been identified and characterized in detail. The initiation of hot corrosion attack induced by molten sulfate leads to Stage 1 “acidic” fluxing and re-precipitation of the protective scale formed initially on the deposit-covered alloy surfaces. Once the protective scale is penetrated, Stage 2 Hot Corrosion is initiated, which is dominated by “basic” fluxing and re-precipitation of the scale in the fused salt. Based on the extensive corrosion information generated from this project, corrosion modeling was performed using non-linear regression analysis. As a result of the modeling efforts, two predictive equations have been formulated, one for furnace walls and the other for superheaters and reheaters. These first-of-the-kind equations can be used to estimate the corrosion rates of boiler tubes based on coal chemistry, alloy compositions, and boiler operating conditions for advanced boiler systems.

  17. Advancing representation of hydrologic processes in the Soil and Water Assessment Tool (SWAT) through integration of the TOPographic MODEL (TOPMODEL) features

    USGS Publications Warehouse

    Chen, J.; Wu, Y.

    2012-01-01

    This paper presents a study of the integration of the Soil and Water Assessment Tool (SWAT) model and the TOPographic MODEL (TOPMODEL) features for enhancing the physical representation of hydrologic processes. In SWAT, four hydrologic processes, which are surface runoff, baseflow, groundwater re-evaporation and deep aquifer percolation, are modeled by using a group of empirical equations. The empirical equations usually constrain the simulation capability of relevant processes. To replace these equations and to model the influences of topography and water table variation on streamflow generation, the TOPMODEL features are integrated into SWAT, and a new model, the so-called SWAT-TOP, is developed. In the new model, the process of deep aquifer percolation is removed, the concept of groundwater re-evaporation is refined, and the processes of surface runoff and baseflow are remodeled. Consequently, three parameters in SWAT are discarded, and two new parameters to reflect the TOPMODEL features are introduced. SWAT-TOP and SWAT are applied to the East River basin in South China, and the results reveal that, compared with SWAT, the new model can provide a more reasonable simulation of the hydrologic processes of surface runoff, groundwater re-evaporation, and baseflow. This study evidences that an established hydrologic model can be further improved by integrating the features of another model, which is a possible way to enhance our understanding of the workings of catchments.

  18. Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

    USGS Publications Warehouse

    Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

    1999-01-01

    Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier

  19. Assessing Impacts of Landuse Changes on Hydrology for the Upper San Pedro Watershed

    EPA Science Inventory

    The assessment of landuse changes on hydrology is essential for the development of sustainable water resource strategies. Specifically, understanding how each land use influences hydrological processes will greatly improve predictability of hydrological consequences to landuse ch...

  20. Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival.

    PubMed

    Cuccarini, Valeria; Erbetta, A; Farinotti, M; Cuppini, L; Ghielmetti, F; Pollo, B; Di Meco, F; Grisoli, M; Filippini, G; Finocchiaro, G; Bruzzone, M G; Eoli, M

    2016-01-01

    MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally,both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features(no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imaging (PWI), diffusion weighted imaging(DWI) and spectroscopy. All the gliomas had histopathological diagnoses. Sensitivity and specificity of cMRI preoperative diagnosis were 78.5 and 38.5 %, respectively, and 85.7 and 53.8 % when a MRI was included, respectively. ROC analysis showed that cut-off values of 1.29 for maximum rCBV, 1.69 for minimum rADC, 2.1 for rCho/Cr ratio could differentiate between LGG and HGG with a sensitivity of 61.5, 53.8, and 53.8 % and a specificity of 54.7, 43 and 64.3 %, respectively. A significantly longer OS was observed in patients with a maximum rCBV<1.46 and minimum rADC>1.69 (80 vs 55 months, p = 0.01; 80 vs 51 months, p = 0.002, respectively). This result was also confirmed when cases were stratified according to pathology (LGG vs HGG). The ability of a MRI to differentiate between LGG and HGG and to predict survival improved as the number of a MRI techniques considered increased. In a selected population of suspected LGG,classification by cMRI underestimated the actual fraction of HGG. aMRI slightly increased the diagnostic accuracy compared to histopathology. However, DWI and PWI were prognostic markers independent of histological grade. PMID:26468137

  1. Hydrologic effects of fire in sagebrush plant communities: Implications for rangeland hydrology and erosion modeling

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Millions of dollars are spent annually in the United States mitigating fire effects on rangeland hydrology and erosion. Rangeland managers and scientists need predictive tools to simulate hydrologic processes dictating post-fire responses, assist mitigation and risk assessments, and predict post-fir...

  2. HOTAIR is a predictive and prognostic biomarker for patients with advanced gastric adenocarcinoma receiving fluorouracil and platinum combination chemotherapy

    PubMed Central

    Zhao, Wei; Dong, Shuang; Duan, Bensong; Chen, Ping; Shi, Lei; Gao, Hengjun; Qi, Haizhi

    2015-01-01

    Accumulating evidence suggests that long non-coding RNA (lncRNA) HOTAIR participates in many types of cancer such as gastric cancer and may confer malignant phenotype to tumor cells. Fluorouracil and platinum combination chemotherapy is the first line therapy for gastric cancer. However, it is still unknown whether HOTAIR influences the outcome of cancer patients treated with chemotherapy. This study aimed to evaluate the association of HOTAIR expression with the prognosis of patients with advanced gastric adenocarcinoma (GA) receiving fluorouracil and platinum based chemotherapy. We examined the levels of HOTAIR in 168 GA samples using quantitative real-time PCR and analyzed its relationship with clinical features and prognosis of patients with advanced GA treated with fluorouracil and platinum based chemotherapy. Compared with paracancerous tissues, HOTAIR was significantly upregulated in GA tissues, especially in more advanced cases. High HOTAIR expression was an independent poor prognostic factor for patients with advanced GA. Further stratification analyses revealed that the association between HOTAIR expression and survival in patients with advanced GA remained significant in the subgroup of patients with TNM stages IIIA and IIIB, poorly differentiated, and smaller tumors. In conclusion, our results provide first evidence that HOTAIR may be served as a biomarker that predicts which patient with advanced GA will benefit from fluorouracil and platinum combination chemotherapy. PMID:26328013

  3. Predicting Advanced Placement Examination Success from FCAT Scores. Research Brief. Volume 0709

    ERIC Educational Resources Information Center

    Froman, Terry; Brown, Shelly; Tirado, Arleti

    2008-01-01

    Advanced Placement courses are offered at M-DCPS for students to acquire college credit or advanced college academic standing. A system has been developed in the past by the College Board to use the PSAT for 10th grade students to estimate their potential for AP Examination success. The same test has recently been applied in this district to 9th…

  4. Predicting Violence Among Forensic-Correctional Populations: The Past 2 Decades of Advancements and Future Endeavors

    ERIC Educational Resources Information Center

    Loza, Wagdy; Dhaliwal, Gurmeet K.

    2005-01-01

    Research on violence prediction during the past 2 decades has evolved appreciably in terms of depicting determinants of violence and developing psychometrically sound actuarial measures to predict the probability of future violent behavior. This article provides a brief synopsis of information on predicting violence gained in the past 2 decades,…

  5. Predicted reliability of aerospace electronics: Application of two advanced probabilistic concepts

    NASA Astrophysics Data System (ADS)

    Suhir, E.

    Two advanced probabilistic design-for-reliability (PDfR) concepts are addressed and discussed in application to the prediction, quantification and assurance of the aerospace electronics reliability: 1) Boltzmann-Arrhenius-Zhurkov (BAZ) model, which is an extension of the currently widely used Arrhenius model and, in combination with the exponential law of reliability, enables one to obtain a simple, easy-to-use and physically meaningful formula for the evaluation of the probability of failure (PoF) of a material or a device after the given time in operation at the given temperature and under the given stress (not necessarily mechanical), and 2) Extreme Value Distribution (EVD) technique that can be used to assess the number of repetitive loadings that result in the material/device degradation and eventually lead to its failure by closing, in a step-wise fashion, the gap between the bearing capacity (stress-free activation energy) of the material or the device and the demand (loading). It is shown that the material degradation (aging, damage accumulation, flaw propagation, etc.) can be viewed, when BAZ model is considered, as a Markovian process, and that the BAZ model can be obtained as the ultimate steady-state solution to the well-known Fokker-Planck equation in the theory of Markovian processes. It is shown also that the BAZ model addresses the worst, but a reasonably conservative, situation. It is suggested therefore that the transient period preceding the condition addressed by the steady-state BAZ model need not be accounted for in engineering evaluations. However, when there is an interest in understanding the transient degradation process, the obtained solution to the Fokker-Planck equation can be used for this purpose. As to the EVD concept, it attributes the degradation process to the accumulation of damages caused by a train of repetitive high-level loadings, while loadings of levels that are considerably lower than their extreme values do not contribute

  6. Prediction of helicopter rotor discrete frequency noise: A computer program incorporating realistic blade motions and advanced acoustic formulation

    NASA Technical Reports Server (NTRS)

    Brentner, K. S.

    1986-01-01

    A computer program has been developed at the Langley Research Center to predict the discrete frequency noise of conventional and advanced helicopter rotors. The program, called WOPWOP, uses the most advanced subsonic formulation of Farassat that is less sensitive to errors and is valid for nearly all helicopter rotor geometries and flight conditions. A brief derivation of the acoustic formulation is presented along with a discussion of the numerical implementation of the formulation. The computer program uses realistic helicopter blade motion and aerodynamic loadings, input by the user, for noise calculation in the time domain. A detailed definition of all the input variables, default values, and output data is included. A comparison with experimental data shows good agreement between prediction and experiment; however, accurate aerodynamic loading is needed.

  7. Understanding hydrological extremes in the Anthropocene

    NASA Astrophysics Data System (ADS)

    Mård, Johanna; Di Baldassarre, Giuliano

    2016-04-01

    Hydrological extremes, from floods to droughts, pose one of the greatest challenges of the 21st century. Many of these challenges are associated with societal interactions with water, as people control or impact hydrological systems in a multitude of ways while they are also being affected and shaped by hydrological extremes, depending on their response to drought and flood events. However, the fact that the human and natural components of freshwater systems interact and co-evolve over time is often not taken into account. There is a need to study the two-way coupling between hydrology and society within a more comprehensive framework for hydrological extremes to anticipate future trajectories in a rapidly changing world. We present an interdisciplinary framework (and concepts) to identify internal controlling variables, processes and feedbacks, and the external system drivers and disturbances of the coupled human-water system with regard to hydrological extremes. To achieve this, the study (i) synthesizes existing research on coupled human-water system focusing on floods and droughts, (ii) analyzes hydrological extremes that have already occurred and their spatiotemporal patterns to investigate what patterns are observed in different regions of the world, and (iii) systematically describe the observed hydrological extremes, their causes and the interactions and feedbacks between hydrology and society. Advancing our understanding of mechanisms and feedbacks driving hydrological extremes is essential to better anticipate how the coupled human-water system will respond to future environmental change.

  8. Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

    PubMed Central

    Yoshihara, Kosuke; Tajima, Atsushi; Yahata, Tetsuro; Kodama, Shoji; Fujiwara, Hiroyuki; Suzuki, Mitsuaki; Onishi, Yoshitaka; Hatae, Masayuki; Sueyoshi, Kazunobu; Fujiwara, Hisaya; Kudo, Yoshiki; Kotera, Kohei; Masuzaki, Hideaki; Tashiro, Hironori; Katabuchi, Hidetaka; Inoue, Ituro; Tanaka, Kenichi

    2010-01-01

    Background Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer. Methodology/Principal Findings Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). Conclusions/Significance The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer. PMID:20300634

  9. The Clinical Significance of MiR-148a as a Predictive Biomarker in Patients with Advanced Colorectal Cancer

    PubMed Central

    Takahashi, Masanobu; Cuatrecasas, Miriam; Balaguer, Francesc; Hur, Keun; Toiyama, Yuji; Castells, Antoni; Boland, C. Richard; Goel, Ajay

    2012-01-01

    Aim Development of robust prognostic and/or predictive biomarkers in patients with colorectal cancer (CRC) is imperative for advancing treatment strategies for this disease. We aimed to determine whether expression status of certain miRNAs might have prognostic/predictive value in CRC patients treated with conventional cytotoxic chemotherapies. Methods We studied a cohort of 273 CRC specimens from stage II/III patients treated with 5-fluorouracil-based adjuvant chemotherapy and stage IV patients subjected to 5-fluorouracil and oxaliplatin-based chemotherapy. In a screening set (n = 44), 13 of 21 candidate miRNAs were successfully quantified by multiplex quantitative RT-PCR. In the validation set comprising of the entire patient cohort, miR-148a expression status was assessed by quantitative RT-PCR, and its promoter methylation was quantified by bisulfite pyrosequencing. Lastly, we analyzed the associations between miR-148a expression and patient survival. Results Among the candidate miRNAs studied, miR-148a expression was most significantly down-regulated in advanced CRC tissues. In stage III and IV CRC, low miR-148a expression was associated with significantly shorter disease free-survival (DFS), a worse therapeutic response, and poor overall survival (OS). Furthermore, miR-148a methylation status correlated inversely with its expression, and was associated with worse survival in stage IV CRC. In multivariate analysis, miR-148a expression was an independent prognostic/predictive biomarker for advanced CRC patients (DFS in stage III, low vs. high expression, HR 2.11; OS in stage IV, HR 1.93). Discussion MiR-148a status has a prognostic/predictive value in advanced CRC patients treated with conventional chemotherapy, which has important clinical implications in improving therapeutic strategies and personalized management of this malignancy. PMID:23056401

  10. Attribution of hydrologic trends using integrated hydrologic and economic models

    NASA Astrophysics Data System (ADS)

    Maneta, M. P.; Brugger, D. R.; Silverman, N. L.

    2014-12-01

    Hydrologic change has been detected in many regions of the world in the form of trends in annual streamflows, varying depths to the regional water table, or other alterations of the hydrologic balance. Most models used to investigate these changes implement sophisticated descriptions of the physical system but use simplified descriptions of the socioeconomic system. These simplifications come in the form of prescribed water diversions and land use change scenarios, which provide little insight into coupled natural-human systems and have limited predictive capabilities. We present an integrated model that adds realism to the description of the hydrologic system in agricultural regions by incorporating a component that updates the allocation of land and water to crops in response to hydroclimatic (water available) and economic conditions (prices of commodities and agricultural inputs). This component assumes that farmers allocate resources to maximize their net revenues, thus justifying the use of optimality conditions to constrain the parameters of an empirical production function that captures the economic behavior of farmers. Because the model internalizes the feedback between climate, agricultural markets, and farming activity into the hydrologic system, it can be used to understand to what extent human economic activity can exacerbate or buffer the regional hydrologic impacts of climate change in agricultural regions. It can also help in the attribution of causes of hydrologic change. These are important issues because local policy and management cannot solve climate change, but they can address land use and agricultural water use. We demonstrate the model in a case study.

  11. Hydrological cycle.

    PubMed

    Gonçalves, H C; Mercante, M A; Santos, E T

    2011-04-01

    The Pantanal hydrological cycle holds an important meaning in the Alto Paraguay Basin, comprising two areas with considerably diverse conditions regarding natural and water resources: the Plateau and the Plains. From the perspective of the ecosystem function, the hydrological flow in the relationship between plateau and plains is important for the creation of reproductive and feeding niches for the regional biodiversity. In general, river declivity in the plateau is 0.6 m/km while declivity on the plains varies from 0.1 to 0.3 m/km. The environment in the plains is characteristically seasonal and is home to an exuberant and abundant diversity of species, including some animals threatened with extinction. When the flat surface meets the plains there is a diminished water flow on the riverbeds and, during the rainy season the rivers overflow their banks, flooding the lowlands. Average annual precipitation in the Basin is 1,396 mm, ranging from 800 mm to 1,600 mm, and the heaviest rainfall occurs in the plateau region. The low drainage capacity of the rivers and lakes that shape the Pantanal, coupled with the climate in the region, produce very high evaporation: approximately 60% of all the waters coming from the plateau are lost through evaporation. The Alto Paraguay Basin, including the Pantanal, while boasting an abundant availability of water resources, also has some spots with water scarcity in some sub-basins, at different times of the year. Climate conditions alone are not enough to explain the differences observed in the Paraguay River regime and some of its tributaries. The complexity of the hydrologic regime of the Paraguay River is due to the low declivity of the lands that comprise the Mato Grosso plains and plateau (50 to 30 cm/km from east to west and 3 to 1.5 cm/km from north to south) as well as the area's dimension, which remains periodically flooded with a large volume of water. PMID:21537597

  12. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

    PubMed Central

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  13. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

    PubMed

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  14. Katul Receives 2012 Hydrologic Sciences Award: Citation

    NASA Astrophysics Data System (ADS)

    Parlange, Marc B.

    2013-10-01

    I am delighted to present Professor Gabriel Katul of Duke University with the 2012 Hydrologic Sciences Award. He has made massive contributions to the understanding and prediction of hydrology, and it is for his work that we (Professors Poporato, Hornberger, Rinaldo, Rodriguez-Iturbe, Brutsaert, and Raupach) nominated him.

  15. Hydrology day

    NASA Astrophysics Data System (ADS)

    Morel-Seytoux, H. J.

    Registration for the Hydrology Day sponsored by the Front Range Branch of AGU on April 23 at Colorado State University in Fort Collins, Colorado, totaled 121 participants, of whom 61 were students.Thirty-one individuals joined the Front Range Branch. Three students from Colorado State University won the awards for best paper in their category: Thomas W. Anzia (Sr.), ‘A Comprehensive Table of Standard Deviates for Confidence Limits on Extreme Events’ Victor Nazareth (M.S.), ‘Aquifer Properties from Single-Hole Aquifer Tests’ and Roy W. Koch (Ph.D.), ‘A Physically Based Derivation of the Distribution of Excess Precipitation.’ Judges for the awards were Dr. Bittinger, Resource Consultants, Fort Collins; George Leavesley and Daniel Bauer, USGS, Water Resources Division, Denver; Scott Tucker, Executive Director, Denver Urban Drainage and Flood Control District; Charles Brendecke, Department of Civil Engineering, Univ. of Colorado, Boulder.

  16. Hydrology team

    NASA Technical Reports Server (NTRS)

    Ragan, R.

    1982-01-01

    General problems faced by hydrologists when using historical records, real time data, statistical analysis, and system simulation in providing quantitative information on the temporal and spatial distribution of water are related to the limitations of these data. Major problem areas requiring multispectral imaging-based research to improve hydrology models involve: evapotranspiration rates and soil moisture dynamics for large areas; the three dimensional characteristics of bodies of water; flooding in wetlands; snow water equivalents; runoff and sediment yield from ungaged watersheds; storm rainfall; fluorescence and polarization of water and its contained substances; discriminating between sediment and chlorophyll in water; role of barrier island dynamics in coastal zone processes; the relationship between remotely measured surface roughness and hydraulic roughness of land surfaces and stream networks; and modeling the runoff process.

  17. Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

    NASA Technical Reports Server (NTRS)

    Wilson, Scott D.; Reid, Terry; Schifer, Nicholas; Briggs, Maxwell

    2011-01-01

    Past methods of predicting net heat input needed to be validated. Validation effort pursued with several paths including improving model inputs, using test hardware to provide validation data, and validating high fidelity models. Validation test hardware provided direct measurement of net heat input for comparison to predicted values. Predicted value of net heat input was 1.7 percent less than measured value and initial calculations of measurement uncertainty were 2.1 percent (under review). Lessons learned during validation effort were incorporated into convertor modeling approach which improved predictions of convertor efficiency.

  18. Prediction Model for Prevalence and Incidence of Advanced Age-Related Macular Degeneration Based on Genetic, Demographic, and Environmental Variables

    PubMed Central

    Seddon, Johanna M.; Reynolds, Robyn; Maller, Julian; Fagerness, Jesen A.; Daly, Mark J.; Rosner, Bernard

    2013-01-01

    Purpose The joint effects of genetic, ocular, and environmental variables were evaluated and predictive models for prevalence and incidence of AMD were assessed. Methods Participants in the multicenter Age-Related Eye Disease Study (AREDS) were included in a prospective evaluation of 1446 individuals, of which 279 progressed to advanced AMD (geographic atrophy or neovascular disease) and 1167 did not progress during 6.3 years of follow-up. For prevalent AMD, 509 advanced cases were compared with 222 controls. Covariates for the incidence analysis included age, sex, education, smoking, body mass index (BMI), baseline AMD grade, and the AREDS vitamin–mineral treatment assignment. DNA specimens were evaluated for six variants in five genes related to AMD. Unconditional logistic regression analyses were performed for prevalent and incident advanced AMD. An algorithm was developed and receiver operating characteristic curves and C statistics were calculated to assess the predictive ability of risk scores to discriminate progressors from nonprogressors. Results All genetic polymorphisms were independently related to prevalence of advanced AMD, controlling for genetic factors, smoking, BMI, and AREDS treatment. Multivariate odds ratios (ORs) were 3.5 (95% confidence interval [CI], 1.7–7.1) for CFH Y402H; 3.7 (95% CI, 1.6 – 8.4) for CFH rs1410996; 25.4 (95% CI, 8.6 –75.1) for LOC387715 A69S (ARMS2); 0.3 (95% CI, 0.1– 0.7) for C2 E318D; 0.3 (95% CI, 0.1– 0.5) for CFB; and 3.6 (95% CI, 1.4 –9.4) for C3 R102G, comparing the homozygous risk/protective genotypes to the referent genotypes. For incident AMD, all these variants except CFB were significantly related to progression to advanced AMD, after controlling for baseline AMD grade and other factors, with ORs from 1.8 to 4.0 for presence of two risk alleles and 0.4 for the protective allele. An interaction was seen between CFH402H and treatment, after controlling for all genotypes. Smoking was independently

  19. Hydrologic Observatories: Design, Operation, and the Neuse Basin Prototype

    NASA Astrophysics Data System (ADS)

    Reckhow, K.; Band, L.

    2003-12-01

    Hydrologic observatories are conceived as major research facilities that will be available to the full hydrologic community, to facilitate comprehensive, cross-disciplinary and multi-scale measurements necessary to address the current and next generation of critical science and management issues. A network of hydrologic observatories is proposed that both develop national comparable, multidisciplinary data sets and provide study areas to allow scientists, through their own creativity, to make scientific breakthroughs that would be impossible without the proposed observatories. The core objective of an observatory is to improve predictive understanding of the flow paths, fluxes, and residence times of water, sediment and nutrients (the "core data") across a range of spatial and temporal scales across `interfaces'. To assess attainment of this objective, a benchmark will be established in the first year, and evaluated periodically. The benchmark should provide an estimate of prediction uncertainty at points in the stream across scale; the general principle is that predictive understanding must be demonstrated internal to the catchment as well as its outlet. The core data will be needed for practically any hydrologic study, yet absence of these data has been a barrier to larger scale studies in the past. However, advancement of hydrologic science facilitated by the network of hydrologic observatories is expected to focus on a set of science drivers, drawn from the major scientific questions posed by the set of NRC reports and refined into CUAHSI themes. These hypotheses will be tested at all observatories and will be used in the design to ensure the sufficiency of the data set. To make the observatories a national (and international) resource, a key aspect of the operation is the support of remote PI's. This support will include a resident staff of scientists and technicians on the order of 10 FTE's, availability of dormitory, laboratory, workshop space for all

  20. Hydrologic Design in the Anthropocene

    NASA Astrophysics Data System (ADS)

    Vogel, R. M.; Farmer, W. H.; Read, L.

    2014-12-01

    In an era dubbed the Anthropocene, the natural world is being transformed by a myriad of human influences. As anthropogenic impacts permeate hydrologic systems, hydrologists are challenged to fully account for such changes and develop new methods of hydrologic design. Deterministic watershed models (DWM), which can account for the impacts of changes in land use, climate and infrastructure, are becoming increasing popular for the design of flood and/or drought protection measures. As with all models that are calibrated to existing datasets, DWMs are subject to model error or uncertainty. In practice, the model error component of DWM predictions is typically ignored yet DWM simulations which ignore model error produce model output which cannot reproduce the statistical properties of the observations they are intended to replicate. In the context of hydrologic design, we demonstrate how ignoring model error can lead to systematic downward bias in flood quantiles, upward bias in drought quantiles and upward bias in water supply yields. By reincorporating model error, we document how DWM models can be used to generate results that mimic actual observations and preserve their statistical behavior. In addition to use of DWM for improved predictions in a changing world, improved communication of the risk and reliability is also needed. Traditional statements of risk and reliability in hydrologic design have been characterized by return periods, but such statements often assume that the annual probability of experiencing a design event remains constant throughout the project horizon. We document the general impact of nonstationarity on the average return period and reliability in the context of hydrologic design. Our analyses reveal that return periods do not provide meaningful expressions of the likelihood of future hydrologic events. Instead, knowledge of system reliability over future planning horizons can more effectively prepare society and communicate the likelihood

  1. Soil erosion predictions from upland areas – a discussion of selected RUSLE2 advances and needs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Obtaining more accurate soil loss estimates from upland areas is important for improving management practices on agricultural fields. Much of the soil erosion prediction research of the last 25 years has been concerned with this goal. The most widely used predictive relationships have been the Unive...

  2. Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Sankararaman, Shankar

    2013-01-01

    Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

  3. Comparison of global optimization approaches for robust calibration of hydrologic model parameters

    NASA Astrophysics Data System (ADS)

    Jung, I. W.

    2015-12-01

    Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

  4. An Integrated Theory for Predicting the Hydrothermomechanical Response of Advanced Composite Structural Components

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

    1977-01-01

    An integrated theory is developed for predicting the hydrothermomechanical (HDTM) response of fiber composite components. The integrated theory is based on a combined theoretical and experimental investigation. In addition to predicting the HDTM response of components, the theory is structured to assess the combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and those of angleplied laminates. The theory developed predicts values which are in good agreement with measured data at the micromechanics, macromechanics, laminate analysis and structural analysis levels.

  5. Grid computing technology for hydrological applications

    NASA Astrophysics Data System (ADS)

    Lecca, G.; Petitdidier, M.; Hluchy, L.; Ivanovic, M.; Kussul, N.; Ray, N.; Thieron, V.

    2011-06-01

    SummaryAdvances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure 2010 work-programme, data and compute infrastructures and their underlying technologies, either oriented to tackle scientific challenges or complex problem solving in engineering, are expected to converge together into the so-called knowledge infrastructures, leading to a more effective research, education and innovation in the next decade and beyond. Grid technology is recognized as a fundamental component of e-Infrastructures. Nevertheless, this emerging paradigm highlights several topics, including data management, algorithm optimization, security, performance (speed, throughput, bandwidth, etc.), and scientific cooperation and collaboration issues that require further examination to fully exploit it and to better inform future research policies. The paper illustrates the results of six different surface and subsurface hydrology applications that have been deployed on the Grid. All the applications aim to answer to strong requirements from the Civil Society at large, relatively to natural and anthropogenic risks. Grid technology has been successfully tested to improve flood prediction, groundwater resources management and Black Sea hydrological survey, by providing large computing resources. It is also shown that Grid technology facilitates e-cooperation among partners by means of services for authentication and authorization, seamless access to distributed data sources, data protection and access right, and standardization.

  6. Towards tracer-aided spatially distributed models of catchment storage and mixing to predict non-stationary hydrologic and biogeochemical response

    NASA Astrophysics Data System (ADS)

    Soulsby, Chris; van Huijgevoort, Marjolein; Dick, Jonathan; Birkel, Christian; Tetzlaff, Doerthe

    2016-04-01

    To develop a spatially distributed understanding of the linkages between storage dynamics, mixing processes and non-stationary watershed response we have used diverse, intensive data sets collected in a small montane catchment to both inform and test hydrological and water quality models. At the core of these efforts has been the use of ~6 years of daily isotope data in precipitation and stream flow to inform the calibration and testing of coupled flow-tracer models that constrain storage estimates, mixing processes and hydrologic fluxes in the dominant landscape units as well as simulating discharge and stream isotopes. LiDAR surveys have been used to extend this approach using a high resolution DTM to facilitate a Spatially distributed Tracer-Aided Rainfall-Runoff model (STARR). This provides a flexible, generic approach that allows us to track and visualise aggregated storage changes, mixing processes, and the fluxes and age distribution of water across spatio-temporal scales. The modelling framework provides a basis for assessing the effects of hydroclimatic variability on the non-stationary nature of catchment hydrological function by simulating the spatial variation in tracer composition of different source waters and flow paths. This is tested against extensive (over 120 sites) synoptic surveys of multiple-tracers in soil water, groundwater and stream water repeated under contrasting states of catchment storage when different flow paths are activated. The modelling approach can reproduce the major spatio-temporal differences in isotopes, dissolved organic (DOC) and alkalinity reasonably well and thus, has potential to be adapted for biogeochemical modelling. This potential is explored in relation to daily DOC simulations over prolonged (2 year) periods. The transferability of the modelling approach to other sites is also tested.

  7. How to predict hydrological effects of local land use change: how the vegetation parameterisation for short rotation coppices influences model results

    NASA Astrophysics Data System (ADS)

    Richter, F.; Döring, C.; Jansen, M.; Panferov, O.; Spank, U.; Bernhofer, C.

    2015-01-01

    Among the different bioenergy sources short rotation coppices (SRC) with poplar and willow trees are one of the mostly promising options in Europe. SRC not only provide woody biomass, but often additional ecosystem services. One known shortcoming is the possible negative effect on groundwater recharge, caused by potentially higher rates of evapotranspiration compared to annual crops. An assessment of land use change by means of hydrological models and taking into account the changing climate can help to minimize negative and maximize positive ecological effects at regional and local scales, e.g. to regional climate and/or to adjacent ecosystems. The present study implemented the hydrological model system WaSim for such assessment. The hydrological analysis requires the adequate description of the vegetation cover to simulate the processes like soil evaporation, interception evaporation and transpiration. The uncertainties in the vegetation parameterisations might result in implausible model results. The present study shows that leaf area index (LAI), stomatal resistance (Rsc) as well as the beginning and length of the growing season are the sensitive parameters when investigating the effects of an enhanced cultivation of SRC on water budget or on groundwater recharge. Mostly sensitive is the description of the beginning of the growing season. When this estimation is wrong, the accuracy of LAI and Rsc description plays a minor role. The analyses done here illustrate that the use of locally measured vegetation parameters like maximal LAI and meteorological variables like air temperature, to estimate the beginning of the growing season, produce better results than literature data or data from remote network stations. However the direct implementation of locally measured or literature data on e.g. stomatal resistance is not always advisable. The adjustment of locally vegetation parameterisation shows the best model evaluation. Additionally the adjusted course of LAI

  8. Hydrologic applications of weather radar

    NASA Astrophysics Data System (ADS)

    Seo, Dong-Jun; Habib, Emad; Andrieu, Hervé; Morin, Efrat

    2015-12-01

    By providing high-resolution quantitative precipitation information (QPI), weather radars have revolutionized hydrology in the last two decades. With the aid of GIS technology, radar-based quantitative precipitation estimates (QPE) have enabled routine high-resolution hydrologic modeling in many parts of the world. Given the ever-increasing need for higher-resolution hydrologic and water resources information for a wide range of applications, one may expect that the use of weather radar will only grow. Despite the tremendous progress, a number of significant scientific, technological and engineering challenges remain to realize its potential. New challenges are also emerging as new areas of applications are discovered, explored and pursued. The purpose of this special issue is to provide the readership with some of the latest advances, lessons learned, experiences gained, and science issues and challenges related to hydrologic applications of weather radar. The special issue features 20 contributions on various topics which reflect the increasing diversity as well as the areas of focus in radar hydrology today. The contributions may be grouped as follows: Radar QPE (Kwon et al.; Hall et al.; Chen and Chandrasekar; Seo and Krajewski; Sandford).

  9. Hydrologic modeling in a marsh-mangrove ecotone: predicting wetland surface water and salinity response to restoration in the Ten Thousand Islands region of Florida, USA

    USGS Publications Warehouse

    Michot, B.D.; Meselhe, E.A.; Krauss, Ken W.; Shrestha, Surendra; From, Andrew S.; Patino, Eduardo

    2015-01-01

    At the fringe of Everglades National Park in southwest Florida, United States, the Ten Thousand Islands National Wildlife Refuge (TTINWR) habitat has been heavily affected by the disruption of natural freshwater flow across the Tamiami Trail (U.S. Highway 41). As the Comprehensive Everglades Restoration Plan (CERP) proposes to restore the natural sheet flow from the Picayune Strand Restoration Project area north of the highway, the impact of planned measures on the hydrology in the refuge needs to be taken into account. The objective of this study was to develop a simple, computationally efficient mass balance model to simulate the spatial and temporal patterns of water level and salinity within the area of interest. This model could be used to assess the effects of the proposed management decisions on the surface water hydrological characteristics of the refuge. Surface water variations are critical to the maintenance of wetland processes. The model domain is divided into 10 compartments on the basis of their shared topography, vegetation, and hydrologic characteristics. A diversion of +10% of the discharge recorded during the modeling period was simulated in the primary canal draining the Picayune Strand forest north of the Tamiami Trail (Faka Union Canal) and this discharge was distributed as overland flow through the refuge area. Water depths were affected only modestly. However, in the northern part of the refuge, the hydroperiod, i.e., the duration of seasonal flooding, was increased by 21 days (from 115 to 136 days) for the simulation during the 2008 wet season, with an average water level rise of 0.06 m. The average salinity over a two-year period in the model area just south of Tamiami Trail was reduced by approximately 8 practical salinity units (psu) (from 18 to 10 psu), whereas the peak dry season average was reduced from 35 to 29 psu (by 17%). These salinity reductions were even larger with greater flow diversions (+20%). Naturally, the reduction

  10. Earthquake prediction

    NASA Technical Reports Server (NTRS)

    Turcotte, Donald L.

    1991-01-01

    The state of the art in earthquake prediction is discussed. Short-term prediction based on seismic precursors, changes in the ratio of compressional velocity to shear velocity, tilt and strain precursors, electromagnetic precursors, hydrologic phenomena, chemical monitors, and animal behavior is examined. Seismic hazard assessment is addressed, and the applications of dynamical systems to earthquake prediction are discussed.

  11. Early prediction of pathological response in locally advanced rectal cancer based on sequential 18F-FDG PET

    PubMed Central

    HATT, MATHIEU; VAN STIPHOUT, RUUD; LE POGAM, ADRIEN; LAMMERING, GUIDO; VISVIKIS, DIMITRIS; LAMBIN, PHILIPPE

    2016-01-01

    Background The objectives of this study were to investigate the predictive value of sequential 18F-FDG PET scans for pathological tumor response grade (TRG) after preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) and the impact of partial volume effects correction (PVC). Methods Twenty-eight LARC patients were included. Responders and non-responders status were determined in histopathology. PET indices [SUV max and mean, volume and total lesion glycolysis (TLG)] at baseline and their evolution after one and two weeks of PCRT were extracted by delineation of the PET images, with or without PVC. Their predictive value was investigated using Mann-Whitney-U tests and ROC analysis. Results Within baseline parameters, only SUVmean was correlated with response. No evolution after one week was predictive of the response, whereas after two weeks all the parameters except volume were, the best prediction being obtained with TLG (AUC 0.79, sensitivity 63%, specificity 92%). PVC had no significant impact on these results. Conclusion Several PET indices at baseline and their evolution after two weeks of PCRT are good predictors of response in LARC, with or without PVC, whereas results after one week are suboptimal. Best predictor was TLG reduction after two weeks, although baseline SUVmean had smaller but similar predictive power. PMID:22873767

  12. Collaboration to develop Cyberinfrastructure for Hydrologic Sciences

    NASA Astrophysics Data System (ADS)

    Valentine, D. W.; Zaslavsky, I.

    2007-05-01

    For the past 3 years, Consortium for Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) has been collaborating with several research universities, and San Diego Supercomputer Center as the technology partner, on developing a cyberinfrastructure for the Hydrologic Sciences, or Hydrologic Information System (HIS). The CUAHSI HIS team has been researching, prototyping, and implementing web services for discovering and accessing a variety of hydrologic data sources, and developing applications for the desktop and for the Web. Several products have been developed: a uniform set of web services for hydrologic data retrieval (WaterOneFlow web services); an information model and database schema for storing hydrologic observations, called the observations data model (ODM); instructions and/or extensions to common desktop applications such as Microsoft Excel and MATLAB for accessing distributed hydrologic data. In collaboration with ESRI, we have developed a Hydrologic Data Access System, a web-accessible map interface to WaterOneFlow web services. The web services have been demonstrated to work within the a modeling framework such as the Open Modeling Interface (OpenMI), as well as within several programming environments, in both Java and .NET. This experience highlighted important compatibility and interoperability issues surrounding the use of web services across languages, computing platforms, and web service toolkits. An important outcome of HIS research and development is the abstract specification of the WaterOneFlow web services. Using this definition, we can present different internet accessible data repositories (USGS NWIS, EPA STORET, DAYMET, NCDC ASOS, etc), as well as local databases following the ODM shema, in a uniform way. This approach simplifies programmatic retrieval and integration of hydrologic data. . Presently, we are focusing on combining the developed tools into a distributable HIS software package which will be implemented at 11

  13. Drought Prediction Site Specific and Regional up to Three Years in Advance

    NASA Astrophysics Data System (ADS)

    Suhler, G.; O'Brien, D. P.

    2002-12-01

    Dynamic Predictables has developed proprietary software that analyzes and predicts future climatic behavior based on past data. The programs employ both a regional thermodynamic model together with a unique predictive algorithm to achieve a high degree of prediction accuracy up to 36 months. The thermodynamic model was developed initially to explain the results of a study on global circulation models done at SUNY-Stony Brook by S. Hameed, R.G. Currie, and H. LaGrone (Int. Jour. Climatology, 15, pp.852-871, 1995). The authors pointed out that on a time scale of 2-70 months the spectrum of sea level pressure is dominated by the harmonics and subharmonics of the seasonal cycle and their combination tones. These oscillations are fundamental to an understanding of climatic variations on a sub-regional to continental basis. The oscillatory nature of these variations allows them to be used as broad based climate predictors. In addition, they can be subtracted from the data to yield residuals. The residuals are then analyzed to determine components that are predictable. The program then combines both the thermodynamic model results (the primary predictive model) with those from the residual data (the secondary model) to yield an estimate of the future behavior of the climatic variable. Spatial resolution is site specific or aggregated regional based upon appropriate length (45 years or more monthly data) and reasonable quality weather observation records. Most climate analysis has been based on monthly time-step data, but time scales on the order of days can be used. Oregon Climate Division 1 (Coastal) precipitation provides an example relating DynaPred's method to nature's observed elements in the early 2000s. The prediction's leading dynamic factors are the strong seasonal in the primary model combined with high secondary model contributions from planet Earth's Chandler Wobble (near 15 months) and what has been called the Quasi-Triennial Oscillation (QTO, near 36 months

  14. Predictive and prognostic significance of circulating endothelial cells in advanced non-small cell lung cancer patients.

    PubMed

    Yuan, Dong-mei; Zhang, Qin; Lv, Yan-ling; Ma, Xing-qun; Zhang, Yan; Liu, Hong-bing; Song, Yong

    2015-11-01

    The aim of this study was to evaluate the predictive and prognostic values of circulating endothelial cells (CECs) in patients with advanced non-small cell lung cancer (NSCLC). A total of 102 newly diagnosed advanced NSCLC patients were enrolled in this study. The amount of CECs was enumerated by flow cytometry (CD45- CD31+ CD146+) at baseline. CEC counts of 56 patients were detected before and after two cycles of chemotherapy. We correlated the baseline and reduction of CECs after therapy with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The CEC level was significantly higher in advanced NSCLC patients, ranging from 57 to 1300 cells/10(5) cells (mean ± SD = 299 ± 221 cells/10(5) cells), than in patients with benign lesions (205 ± 97 cells/10(5) cells) and healthy volunteers (117 ± 33 cells/10(5) cells). When the cutoff value of CEC counts was 210 cells/10(5) cells, there was no significant association between CEC counts and OR/PFS/OS of the enrolled patients. However, patients with CEC response after chemotherapy have more chances to achieve OR (P < 0.001), and such patients showed longer PFS (P = 0.048) and OS (P = 0.018) than those without CEC response. In the multivariate analysis, the independent prognostic roles of brain metastasis (HR 6.165, P = 0.001), and CEC response (HR 0.442, P = 0.044) were found. The CEC counts could be considered as diagnostic biomarker for advanced NSCLC patients. And the reduction of CECs after treatment might be more ideal than the baseline CEC counts as a predictive or prognostic factor in patients treated with chemotherapy or anti-angiogenic therapy. PMID:26084612

  15. Improving standard practices for prediction in ungauged basins: Bayesian approach

    NASA Astrophysics Data System (ADS)

    Prieto, Cristina; Le-Vine, Nataliya; García, Eduardo; Medina, Raúl

    2015-04-01

    In hydrological modelling, the prediction of flows in ungauged basins is still a defiance. Among the different alternatives to quantify and reduce the uncertainty in the predictions, a Bayesian framework has proven to be advantageous. This framework allows flow prediction in ungauged basins based on regionalised hydrological indices. Being grounded on probability theory, the procedure requires a number of assumptions and decisions to be made. Among the most important ones are 1) selection of represe