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Monday, July 20, 2020 | History

2 edition of Statistical models for estimating daily streamflow in Michigan found in the catalog.

Statistical models for estimating daily streamflow in Michigan

David J Holtschlag

Statistical models for estimating daily streamflow in Michigan

by David J Holtschlag

  • 29 Want to read
  • 24 Currently reading

Published by Dept of the Interior, U.S. Geological Survey in Lansing, Mich .
Written in English

    Subjects:
  • Stream measurements -- Michigan,
  • Streamflow -- Michigan -- Mathematical models

  • Edition Notes

    Statementby D.J. Holtschlag, and Habib Salehi ; prepared in cooperation with Michigan Department of Natural Resources
    SeriesWater-resources investigations report -- 91-4194
    ContributionsSalehi, Habib, Michigan. Dept. of Natural Resources, Geological Survey (U.S.)
    The Physical Object
    Paginationiv, 48 p. :
    Number of Pages48
    ID Numbers
    Open LibraryOL13614248M

      Season-ahead statistical forecast models have been developed for precipitation and streamflow. In each case, asuite of large -scale (e.g. sea surface temperatures, sea level pressures) and local (e.g. soil moisture) seasonahead predictors from the historical record - evaluated for have been use in a hybrid Principal Components-Autoregressive June Professor QJ Wang obtained his BE in from Tsinghua University at Beijing with a “Graduate of Excellence” Award. In Ireland, he completed his MSc in and PhD in at University College Galway. QJ worked briefly as a Postdoctoral Fellow with Professor James Dooge at University College Dublin, before returning to University College Galway to take up a Lecturer ://

    Development of Regression Models to Estimate Flow Duration Statistics at Ungaged Streams in Oklahoma Using a Regional Approach from continuous streamflow data at gaged locations with 10 or more years of record, to physical and climatic basin characteristics. The accuracy of regressions for estimating flow duration statistics in western () This paper presents a new approach for the modeling of dry period interarrival times. The new model targets dry period interarrival times directly and uses a combination of the empirical model and (ASCE)HE

    Ghosh S, Mujumdar PP () Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv Water Resour – CrossRef Google Scholar Gosain AK, Rao Sandhya, Basuray Debajit () Climate change impact assessment on hydrology of Indian River ://   Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about


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Statistical models for estimating daily streamflow in Michigan by David J Holtschlag Download PDF EPUB FB2

STATISTICAL MODELS FOR ESTIMATING DAILY STREAMFLOW IN MICHIGAN By D.J. Holtschlag, U.S. Geological Survey, and Habib Salehi, Michigan State University U.S. GEOLOGICAL SURVEY Water Resources Investigations Report 91 Prepared in cooperation with the MICHIGAN DEPARTMENT OF NATURAL RESOURCES Lansing, Michigan Get this from a library.

Statistical models for estimating daily streamflow in Michigan. [David J Holtschlag; Habib Salehi; Michigan. Department of Natural Resources.; Geological Survey (U.S.)] Abstract. Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan.

Stations were paired by randomly choosing a station operated in at which 10 or more years of continuous flow data had been Statistical models for estimating daily streamflow in Michigan book and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are ://   statistical models for estimating daily streamflow in michigan hydrology 01/01/ Used in Geological Survey Report on Statistical Models for Estimating Daily Streamflow in Michigan - Automatic Forecasting Systems   The New York Streamflow Estimation Tool produces a complete estimated daily mean time series from which daily flow statistics can be estimated and a means for quantitative flow assessments at ungaged locations to restore and maintain the chemical, physical, and biological integrity of   STATISTICAL MODELS FOR ESTIMATING FLOVf CHARACTERISTICS OF MICHIGAN STREAMS By D.J.

Holtschlag, U.S. Geological Survey, and Hope M. Croskey, Michigan Department of Natural Resources U.S. GEOLOGICAL SURVEY Water-Resources Investigations Report Prepared jointly with MICHIGAN DEPARTMENT OF NATURAL RESOURCES,   Statistical models of streamflow have also been developed and are being used to improve calibration of the National Hydrologic Model.

Surface Water Trends Analysis One of the Water Census goals as called for in the SECURE Water Act is to analyze historic trends and provide annual updates of river basin :// /water-resources/science/national-water-census-streamflow. A regression model for computing index flows describing the median flow for the summer month of lowest flow in Michigan - Statistical Models for Estimating Daily Streamflow in Michigan - Streambed Stability and Scour Potential at Selected Bridge Sites in Michigan - Pleistocene Proboscideans and Michigan.

A Proposed Streamflow Data Program for Michigan: Geological Survey Open-File Report; 69 P, 5 Fig, 8 Tab, 8Rrf, 1 Append. D.J., Salehi, H., Statistical Models for Estimating Daily Streamflow in Michigan: Available from the US Geological Survey, Books and Open-File Reports Section; BoxDenver, CO Water-Resources Statistical Models for Estimating Daily Streamflow in Michigan - Streambed Stability and Scour Potential at Selected Bridge Sites in Michigan - Pleistocene Proboscideans and Michigan salt deposits - Changes in water quality of Michigan streams near urban areas - Cost Effectiveness of Stream-Gaging Program in Michigan.

Michigan's environmental protection and recreation bond programs: STRMDEPLan extended version of STRMDEPL with additional analytical solutions to calculate streamflow depletion by nearby pumping wells by Howard W Reeves Statistical models for estimating daily streamflow in Michigan by David J Holtschlag Daily mean streamflow and catchment average monthly precipitation totals were provided by the National River Flow Archive (NRFA) [Marsh and Hannaford, ].For streamflow, each individual month was required to have at least 25 days of valid daily mean flow observations for a monthly mean flow (expressed in m 3 /s) to be calculated and used in this ://   We applied a watershed model of the Huron River watershed in Michigan, USA, built with SWAT to estimate daily streamflow, drove the model with climate data from both statistically-generated climate scenarios and physically-based climate models, and evaluated four indices to quantify separate aspects of flood risk.

Methods Study area   A majority of high-groundwater-level models in the East region used streamflow or base flow as the explanatory variable, which generally yielded the strongest models overall on the basis of a mean NSE of for streamflow and for base flow—among those, the majority of models used same month (1-month) streamflow or base flow as the best Baseflow plays an important role in maintaining streamflow.

Seventeen gauged watersheds and their characteristics were used to develop regression models for annual baseflow and baseflow index (BFI) estimation in Michigan. Baseflow was estimated from daily streamflow records using the two-parameter recursive digital filter method for baseflow separation of the Web-based Hydrograph Analysis Tool The daily streamflow forecasts (1-day-ahead) during the validation period (from 1 January to 31 December ) from ANFIS, the best WS model, and the final BWS model (the expected BMA mean) were compared at the two sites in the form of a hydrograph, as shown in Fig.

:// PDF | Daily streamflows are often represented by flow duration curves (FDCs), which illustrate the frequency with which flows are equaled or exceeded. | Find, read and cite all the research you The study watershed only contained observed streamflow data at four of subbasins. Therefore, the calibrated SWAT model outputs were used to judge the predictive data-driven models’ capability in estimating flow at local and global scales.

SWAT can estimate streamflow beyond the four gauging stations for all stream segments in the ://   periods associated with flood peaks of different magnitudes from recorded historical floods using statistical method.

The selected method is Gumbel extreme value distribution which is widely used for flood frequency analysis. Never Mujere / International Journal on Computer Science and Engineering (IJCSE) ISSN: Vol.

3 No. 7 July Streamflow modelling results from the GR4H and PDM hydrological models were evaluated in two Australian sub-catchments, using (1) calibration to streamflow and (2) joint-calibration to streamflow.

where. L T is the total flux; L i is the predicted flux for day i from equation 2; n is the number of days; Δt is the daily time step; and Σ is a summation. The average daily discharge was used to estimate L use of a daily time-step should be adequate for the calculation of annual fluxes of large rivers because streamwater concentration and discharge do not change radically within a The quality of the model is assessed by testing several statistics such as mean, standard deviation, and skewness of streamflow, and especially the statistics of the peak discharge, flood volumes and low flow volumes over fixed durations.

The model is applied to the daily streamflow of 1. Introduction [2] Daily streamflow time series are critical to a very broad range of hydrologic problems, including, but not limited to rainfall runoff model calibration and the determination of ecological needs for aquatic habitat.

Daily streamflow time series are readily obtained from gaged catchments; however, streamflow information is commonly needed at catchments for which no measured