scholarly journals Developing Sediment Yield Prediction Equations for Small Catchments in Tanzania

10.5772/18430 ◽  
2011 ◽  
Author(s):  
Preksedis Marco
Keyword(s):  
Sediment Yield ◽  
Yield Prediction ◽  
Prediction Equations ◽  
Small Catchments

Regionalization of sediment rating curve for sediment yield prediction in ungauged catchments

2013 ◽  
Vol 46 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Sokchhay Heng ◽  
Tadashi Suetsugi
Keyword(s):  
Sediment Yield ◽  
Model Performance ◽  
Data Uncertainty ◽  
Spatial Proximity ◽  
Yield Prediction ◽  
Rating Curve ◽  
Ungauged Catchments ◽  
Probability Curve ◽  
Sediment Rating Curve ◽  
Almost All

The main objective of this research is to regionalize the sediment rating curve (SRC) for subsequent sediment yield prediction in ungauged catchments (UCs) in the Lower Mekong Basin. Firstly, a power function-based SRC was fitted for 17 catchments located in different parts of the basin. According to physical characteristics of the fitted SRCs, the sediment amount observed at the catchment outlets is mainly transported by several events. This also indicates that clockwise hysteretic phenomenon of sediment transport is rather important in this basin. Secondly, after discarding two outlier catchments due to data uncertainty, the remaining 15 catchments were accounted for the assessment of model performance in UCs by means of jack-knife procedure. The model regionalization was conducted using spatial proximity approach. As a result of comparative study, the spatial proximity approach based on single donor catchment provides a better regionalization solution than the one based on multiple donor catchments. By considering the ideal alternative, a satisfactory result was obtained in almost all the modeled catchments. Finally, a regional model which is a combination of the 15 locally fitted SRCs was established for use in the basin. The model users can check the probability that the prediction results are satisfactory using the designed probability curve.

Calibration and Validation of SWAT for Field-scale Sediment-Yield Prediction

2006 ◽  
Author(s):  
Devanand Maski ◽  
Kyle R Mankin ◽  
Shilpa Anand ◽  
Keith A Janssen ◽  
Gary M Pierzynski
Keyword(s):  
Sediment Yield ◽  
Yield Prediction ◽  
Field Scale ◽  
Calibration And Validation

scholarly journals Empirical Yield Prediction Equations for Plantation-Grown European Larch in Maine

1996 ◽  
Vol 13 (1) ◽  
pp. 37-40 ◽  
Author(s):  
Daniel W. Gilmore ◽  
Russell D. Briggs
Keyword(s):  
Variable Density ◽  
Yield Prediction ◽  
Prediction Equations ◽  
European Larch ◽  
Forecasting Models ◽  
Level Data ◽  
Yield Forecasting

Abstract Density-free and variable-density empirical yield prediction equations were constructed from stand level data collected from 28 plots established in all of the European latch plantations known to have been successfully established on abandoned farmlands in central Maine prior to 1983. Results can be used directly to predict future plantation yields or incorporated in yield forecasting models. North. J. Appl. For. 13(1):37-40.

scholarly journals Sediment Yield Prediction in Tanzania:Case study of Dodoma District Catchments

2008 ◽  
Vol 31 (1) ◽  
pp. 63-71
Author(s):  
Mathew K. Mulemgera
Keyword(s):  
Water Resources ◽  
Soil Erosion ◽  
Sediment Yield ◽  
Development Projects ◽  
Yield Prediction ◽  
Erosion Prediction ◽  
Yield Modeling ◽  
Physically Based ◽  
Physically Based Models ◽  
Rating Curves

Soil erosion and sedimentation is serious and is resulting in rapid sedimentation of streams and reservoirs in Tanzania. This makes sediment yield modeling important before undertaking water resources development projects, although it has so far not been done in the country. At present sediment yield prediction can be done using physically based models,empirical soil erosion and sediment yield models, and sediment rating curves. The physically based models contain numerous parameters that are difficult to measure or estimate, making them inappropriate in developing countries like Tanzania. The rating curves give rough estimates while the empirical sediment yield models need runoff data that is notavailable in Tanzania. The revised USLE parametric (empirical) model that had been tested and found to be suitable for soil erosion prediction was selected for sediment yield prediction study reported in this paper. The testing of the model has shown to give acceptable results. For sediment measurements ranging from about 7 t/ha.year to 11 t/ha.year, the model predicted sediments yields ranging from about 6 t/ha.year to 12 t/ha.year, withcoefficent of determination (R 2 ) equal to about 0.53. Since soil erosion and sedimentation of water resources are serious problems in Tanzania, sediment yield modeling using the RUSLE can be a very useful tool in the country when used.

scholarly journals COMPARATIVE EFFICACY OF SOME EMPIRICAL MODELS TO ESTIMATE SEDIMENT YIELD IN SMALL CATCHMENTS

2018 ◽  
Vol 64 (2) ◽  
Author(s):  
Ebrahim BROOSHKEH ◽  
Reza SOKOUTI ◽  
Mahmoud ARAB KHEDRI ◽  
Saeed NABI PEY LASHKARIAN
Keyword(s):  
Sediment Yield ◽  
Empirical Models ◽  
Comparative Efficacy ◽  
Small Catchments

SOIL EROSION AND SEDIMENT YIELD PREDICTION ACCURACY USING WEPP

2004 ◽  
Vol 40 (2) ◽  
pp. 289-297 ◽  
Author(s):  
John M. Laflen ◽  
Dennis C. Flanagan ◽  
Bernard A. Engel
Keyword(s):  
Soil Erosion ◽  
Sediment Yield ◽  
Prediction Accuracy ◽  
Yield Prediction
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