The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Khaled H Hamed - One of the best experts on this subject based on the ideXlab platform.
-
enhancing the effectiveness of prewhitening in trend analysis of Hydrologic Data
Journal of Hydrology, 2009Co-Authors: Khaled H HamedAbstract:Summary Prewhitening of Hydrologic as well as other types of natural time series has been suggested in the literature to eliminate the adverse effect of autocorrelation on the results of trend tests. It has been suggested in a recent study that prewhitening is not recommended when a true trend exists in the Data. When prewhitening is applied, there has also been a debate on whether or not to remove an apparent trend before estimating the autocorrelation parameter ρ to ensure effective prewhitening. This is because while failing to remove an apparent trend before estimating ρ results in loss of power due to overestimation of ρ when a true trend exists in the Data, it is also true that removing an apparent trend before estimating ρ results in loss of significance due to underestimation of ρ when no trend exists in the Data. In this study, the applicability of prewhitening in the possible presence of a true trend is first established. It is then shown that simultaneous estimation of the trend slope and the autocorrelation coefficient, followed by correction of bias in the correlation coefficient largely eliminates the under/over-estimation of ρ within the limits of sampling variations, thus greatly enhancing the effectiveness of prewhitening. It is also shown that careful inference about the correlation model is critical for effective prewhitening. A comparison between the results obtained with and without bias correction is presented for a case study of trends in riverflow series from different parts of the world. The results emphasize the importance of bias correction in small samples, as well as the importance of careful choice of a serial correlation model for the Data, especially in the case of long time series.
-
trend detection in Hydrologic Data the mann kendall trend test under the scaling hypothesis
Journal of Hydrology, 2008Co-Authors: Khaled H HamedAbstract:Summary The subject of trend detection in Hydrologic Data has received a great deal of attention lately, especially in connection with the anticipated changes in global climate. However, climatic variability, which is reflected in Hydrologic Data, can adversely affect trend test results. The scaling hypothesis has been recently proposed for modeling such variability in Hydrologic Data. In this paper, the Mann–Kendall test, which is widely used to detect trends in Hydrologic Data, is modified to account for the effect of scaling. Exact expressions for the mean and variance of the test statistic are derived under the scaling hypothesis, and the Normal distribution is shown to remain a reasonable approximation. A procedure for estimating the modified variance from observed Data is also outlined. The modified test is applied to a group of 57 worldwide total annual river flow time series from the Database of the Global Runoff Data Centre in Koblenz, Germany, that were shown in an earlier study to exhibit significant trends in annual maximum flow. The results show a considerable reduction in the number of stations with significant trends when the effect of scaling is taken into account. These results indicate that the evidence of real trends in Hydrologic Data is even weaker than suggested by earlier studies, although highly significant increasing trends seem to be more common than negative ones. It is also shown that admitting scaling in the modified test helps to avoid discrepancies found in some previous studies, such as the existence of significant opposite trends in neighboring stations, or in different segments of the same time series.
-
Trend detection in Hydrologic Data: The Mann–Kendall trend test under the scaling hypothesis
Journal of Hydrology, 2008Co-Authors: Khaled H HamedAbstract:Summary The subject of trend detection in Hydrologic Data has received a great deal of attention lately, especially in connection with the anticipated changes in global climate. However, climatic variability, which is reflected in Hydrologic Data, can adversely affect trend test results. The scaling hypothesis has been recently proposed for modeling such variability in Hydrologic Data. In this paper, the Mann–Kendall test, which is widely used to detect trends in Hydrologic Data, is modified to account for the effect of scaling. Exact expressions for the mean and variance of the test statistic are derived under the scaling hypothesis, and the Normal distribution is shown to remain a reasonable approximation. A procedure for estimating the modified variance from observed Data is also outlined. The modified test is applied to a group of 57 worldwide total annual river flow time series from the Database of the Global Runoff Data Centre in Koblenz, Germany, that were shown in an earlier study to exhibit significant trends in annual maximum flow. The results show a considerable reduction in the number of stations with significant trends when the effect of scaling is taken into account. These results indicate that the evidence of real trends in Hydrologic Data is even weaker than suggested by earlier studies, although highly significant increasing trends seem to be more common than negative ones. It is also shown that admitting scaling in the modified test helps to avoid discrepancies found in some previous studies, such as the existence of significant opposite trends in neighboring stations, or in different segments of the same time series.
Bjc Perera - One of the best experts on this subject based on the ideXlab platform.
-
Hydrological Data monitoring for urban stormwater drainage systems
Journal of Hydrology, 2001Co-Authors: U. K. Maheepala, A. K Takyi, Bjc PereraAbstract:Abstract Good quality Hydrologic Data are required to develop and calibrate simulation models, which are often used to plan, design and upgrade urban stormwater drainage systems. These good quality Data can be obtained from a successful Data collection program. This paper describes the issues that should be considered in conducting such a successful and cost-effective Hydrologic Data monitoring program. The issues addressed include considerations given in the selection of suitable monitoring sites, the selection of appropriate measuring equipment and the calibration and installation of the measuring equipment. Furthermore, the techniques that were used to test the accuracy and consistency of the measured Data are also outlined. In addition to these issues, the effects of rainfall measuring resolution of pluviometers and Data logging interval of flowmeters on the accuracy of rainfall and stormwater runoff Data, and computer modelling results were investigated. These investigations revealed that tipping bucket resolutions up to 0.5 mm would give reasonably accurate results in urban stormwater modelling. A two-minute Data logging interval was found to be suitable for flow Data monitoring. The results of the investigations also suggest that a combination of low cost simple flow measurements and limited high cost sophisticated measurements can be used to reduce the Data acquisition cost without compromising the accuracy of flow hydrographs measured in stormwater conduits.
-
Monthly Hydrologic Data generation by disaggregation
Journal of Hydrology, 1996Co-Authors: Shiroma Maheepala, Bjc PereraAbstract:Abstract Stochastically generated Hydrologic Data have been used in the past by water worhorities world-wide for long-term planning of water resources development projects. These Data are also currently being used in short- and medium-term planning and operation of water resource systems. For valid and realistic results, it is necessary that the generated Data sequences preserve all statistical properties of historical Data. This paper presents an improved disaggregation method for generation of alternative sequences of monthly Hydrologic Data. The method is designed explicitly to preserve the over-year monthly serial and cross correlations, in addition to other monthly and annual parameters of the historic sequence. The method is applied to both single-site and multi-site cases, and compared with two other disaggregation models that are used in Australia. The comparison of results shows that the developed method satisfactorily preserves both monthly and annual statistical parameters of the historic Data sequences including the over-year monthly correlations.
Dennis Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
-
a multiscale ensemble filtering system for Hydrologic Data assimilation part i implementation and synthetic experiment
Journal of Hydrometeorology, 2009Co-Authors: Ming Pan, Dennis Mclaughlin, Eric F Wood, Dara Entekhabi, Lifeng LuoAbstract:Abstract The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a Hydrologic Data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A...
-
an integrated approach to Hydrologic Data assimilation interpolation smoothing and filtering
Advances in Water Resources, 2002Co-Authors: Dennis MclaughlinAbstract:The Hydrologic Data assimilation problem can be posed in a probabilistic framework that emphasizes the need to account for uncertainty when combining different sources of information. This framework indicates where approximations need to be introduced and provides a way to compare alternative Data assimilation methods. When discussing Data assimilation it is useful to distinguish interpolation, smoothing, and filtering problems. Interpolation is illustrated here with an example based on multi-scale estimation of rainfall during the TOAGA-COARE field experiment. Smoothing is illustrated with a variational soil moisture estimation algorithm applied to the SGP97 field experiment. Filtering is illustrated with an ensemble Kalman filter, also applied to the SGP97 experiment. All of these Data assimilation algorithms implicitly rely on linear Gaussian assumptions that can only be expected to apply in special cases. Although more general nonlinear Data assimilation methods are available they are not practical for the very large problems frequently encountered in hydrology. Future research in Hydrologic Data assimilation will be need to focus on the issue of high dimensionality and on the need for more realistic descriptions of model and measurement error. This effort will be most successful if the modeling and Data assimilation problems are approached in a coordinated way.
-
Hydrologic Data assimilation with the ensemble kalman filter
Monthly Weather Review, 2002Co-Authors: Rolf H Reichle, Dennis Mclaughlin, Dara EntekhabiAbstract:Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter’s performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust Data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.
-
recent developments in Hydrologic Data assimilation
Reviews of Geophysics, 1995Co-Authors: Dennis MclaughlinAbstract:Data assimilation is a term that is most closely associated with the atmospheric sciences. It arises from the meteorological custom of constructing daily weather maps which show how environmental variables such as pressure and wind velocity vary over space (see Daley [1991] for a good historical review). Such maps are useful both for short-term forecasting, where they provide initial conditions for numerical weather prediction models, and for more long-term climatic analysis, where they can be used to reveal regional trends [Bengtson and Shukla, 1988; National Research Council, 1991; Shubert et al, 1993]. For many years weather maps were derived from a limited number of measurements collected from surface stations and radiosondes. The mapping process required a good understanding of atmospheric physics and was typically carried out by hand, using the methods of ‘subjective analysis’. In the first half of the twentieth century efforts were made to standardize and automate meteorological mapping. The quantitative procedures which grew out of these efforts were commonly called ‘objective analysis’ methods [Gandin, 1963]. Objective analysis algorithms provide spatially distributed descriptions of atmospheric conditions which are consistent both with observed Data and with kinematic (‘diagnostic’) principles (such as the geostrophic requirement that the pressure gradient and Coriolis force must balance). These algorithms produce ‘snapshots’ portraying conditions everywhere in a given region at a particular time.
Jeffery S. Horsburgh - One of the best experts on this subject based on the ideXlab platform.
-
Data visualization and analysis within a Hydrologic information system integrating with the r statistical computing environment
Environmental Modelling and Software, 2014Co-Authors: Jeffery S. Horsburgh, Stephanie L ReederAbstract:This paper presents a prototype software system for visualization and analysis of Hydrologic Data that provides interoperability between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the R statistical computing environment. By linking these two systems within a single desktop software application, an integrated Hydrologic Data management and analysis environment has been created that simplifies the process used by scientists and engineers to find, access, organize, and analyze the Hydrologic Data needed in modeling and managing Hydrologic and environmental systems. The implementation of this work is a software plug-in for the CUAHSI HIS HydroDesktop software system called HydroR. We describe the design, graphical user interface, and implementation of the HydroR plug-in. An example application of HydroR is presented in which total suspended solids concentrations are modeled for the Little Bear River using a regression developed from turbidity and total suspended solids observations downloaded from the CUAHSI HIS using HydroDesktop. Finally, we conclude with a summary of our experience in developing interoperability between HIS and R and suggest future developments that can extend the capabilities we have developed. We developed a software system that integrates the CUAHSI HIS and R.HydroR combines Data discovery and management with analysis and visualization in a single software environment.HydroR reduces effort required to access Data and transfer it into an analysis environment.HydroR promotes repeatable analyses and may reduce potential errors.
-
HydroShare: Advancing Collaboration through Hydrologic Data and Model Sharing
2014Co-Authors: David G. Tarboton, Ray Idaszak, Jeffery S. Horsburgh, J. Heard, Daniel P. Ames, Jonathan L. Goodall, Lawrence E. Band, Venkatesh Merwade, Alva L. Couch, Jennifer ArrigoAbstract:HydroShare is an online, collaborative system being developed for open sharing of Hydrologic Data and models. The goal of HydroShare is to enable hydrology researchers to easily discover and access Hydrologic Data and models, retrieve them to their desktop for local analysis and perform analyses in a distributed computing environment that may include grid, cloud or high performance computing. Users may also share and publish outcomes (Data, results or models) into HydroShare, using the system as a collaboration platform. HydroShare is expanding the Data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of Data accommodated. HydroShare will take advantage of emerging social media functionality to enhance information about and collaboration around Hydrologic Data and models. One of the fundamental concepts in HydroShare is that of a resource. All content is represented using a Resource Data Model that has elements common to all resources as well as elements specific to the types of resources HydroShare will support. These will include different Data types used in the hydrology community and models and workflows that require metaData on execution functionality. The HydroShare web interface and social media functions are being developed using the Django web application framework. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic Datasets. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated Data content and perform rule-based background actions on Data and model resources, including the execution of models and workflows. This paper introduces the HydroShare functionality developed to date and elaborates on the representation of Hydrologic Data and models in this system as resources for collaboration.
-
hydroshare an online collaborative environment for the sharing of Hydrologic Data and models
AGUFM, 2013Co-Authors: David G. Tarboton, Ray Idaszak, Jeffery S. Horsburgh, Daniel P. Ames, Lawrence E. Band, Venkatesh Merwade, Alva L. Couch, Jennifer Arrigo, J L Goodall, Richard P HooperAbstract:David Tarboton (PI), Ray Idaszak, Daniel P. Ames, Jeff Horsburgh, Jon Goodall, Larry Band, Venkatesh Merwade, Jeff Heard, Carol Song, Alva Couch, David Valentine, Rick Hooper, Jennifer Arrigo, David Maidment, Tim Whiteaker, Alex Bedig, Laura Christopherson, Pabitra Dash, Tian Gan, Tony Castronova, Karl Gustafson, Stephen Jackson, Cuyler Frisby, Stephanie Mills, Brian Miles, Jon Pollak, Stephanie Reeder, Yaping Xiao, Lan Zhao
-
hydrodesktop web services based software for Hydrologic Data discovery download visualization and analysis
Environmental Modelling and Software, 2012Co-Authors: Daniel P. Ames, Jeffery S. Horsburgh, Yang Cao, Jiři Kadlec, Timothy L Whiteaker, David L ValentineAbstract:Discovering and accessing Hydrologic and climate Data for use in research or water management can be a difficult task that consumes valuable time and personnel resources. Until recently, this task required discovering and navigating many different Data repositories, each having its own website, query interface, Data formats, and descriptive language. New advances in cyberinfrastructure and in semantic mediation technologies have provided the means for creating better tools supporting Data discovery and access. In this paper we describe a freely available and open source software tool, called HydroDesktop, that can be used for discovering, downloading, managing, visualizing, and analyzing Hydrologic Data. HydroDesktop was created as a means for searching across and accessing Hydrologic Data services that have been published using the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS). We describe the design and architecture of HydroDesktop, its novel contributions in web services-based Hydrologic Data search and discovery, and its unique extensibility interface that enables developers to create custom Data analysis and visualization plug-ins. The functionality of HydroDesktop and some of its existing plug-ins are introduced in the context of a case study for discovering, downloading, and visualizing Data within the Bear River Watershed in Idaho, USA.
Dara Entekhabi - One of the best experts on this subject based on the ideXlab platform.
-
Hydrologic Data assimilation with a hillslope scale resolving model and l band radar observations synthetic experiments with the ensemble kalman filter
Water Resources Research, 2012Co-Authors: Alejandro N Flores, Rafael L Bras, Dara EntekhabiAbstract:United States. Army Research Office (U.S. Army RDECOM ARL Army Research Office under grant W911NF-04-1-0119)
-
a multiscale ensemble filtering system for Hydrologic Data assimilation part i implementation and synthetic experiment
Journal of Hydrometeorology, 2009Co-Authors: Ming Pan, Dennis Mclaughlin, Eric F Wood, Dara Entekhabi, Lifeng LuoAbstract:Abstract The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a Hydrologic Data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A...
-
Hydrologic Data assimilation with the ensemble kalman filter
Monthly Weather Review, 2002Co-Authors: Rolf H Reichle, Dennis Mclaughlin, Dara EntekhabiAbstract:Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter’s performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust Data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.
