Search Results Heading

MBRLSearchResults

mbrl.module.common.modules.added.book.to.shelf
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
    Done
    Filters
    Reset
  • Discipline
      Discipline
      Clear All
      Discipline
  • Is Peer Reviewed
      Is Peer Reviewed
      Clear All
      Is Peer Reviewed
  • Item Type
      Item Type
      Clear All
      Item Type
  • Subject
      Subject
      Clear All
      Subject
  • Year
      Year
      Clear All
      From:
      -
      To:
  • More Filters
1 result(s) for "Boro, Samrat"
Sort by:
River system sediment rating curve parameter estimation via integrated models
Continuous water and sediment flow monitoring across river cross sections is essential for the management of flood- and sediment-related problems in watersheds. The sediment rating curve (SRC) estimates missing or uncertain sediment flow via its corresponding water discharge. Generally, a power form of relationship correlates the two quantities. The log-transformed water discharge and sediment discharge data were used to depict the SRCs developed in the present study. SRC parameter estimation via least squares regression using at-site dataset pairs can be found in the literature. However, the availability of reliable datasets at the site limits model applicability. This method does not describe the SRC on the basis of the continuity aspects of river system flow characteristics. Therefore, the current study proposes integrated SRC estimation models (Model 2 and Model 3) using modified Muskingum equations abiding by the spatial and temporal continuity of the entire river system state. These models are derived from streamflow storage balance criteria and ensure flow continuity norms. Moreover, Model 3 considers an inverse power form of the relationship depicting the water flow characteristics that govern the sediment transport phenomena through the river system. Standalone models for SRC parameter estimation (Model 1) were also developed for comparison among all three models via the root mean square error (RMSE), NRMSE (normalized root mean square error) and coefficient of determination (R2). The Mahanadi River system within Chhattisgarh state, India comprises five sections at tributaries, and the main channel was considered for the study. The improved NRMSE by Model 2 (7.53%) and Model 3 (7.14%) at Rajim and Model 3 (3.44%) at Bamnidhi in comparison to Model 1 at Rajim (9.19%) and Bamnidhi (4.80%) encouraged the application of integrated models for SRC estimation in river systems. Moreover, Model 3 outperformed Model 2 in some cases where the sediment transport process may be governed by water flow characteristics. [Display omitted] •Sediment rating curve estimate for entire river network replacing standalone model.•Muskingum model applications ensuring flow continuity, is recommended to adopt.•Water flow characteristics parameters influence sediment water relationship in river.•Both integrated models outperformed standalone model at upstream bounding section.