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

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.

scholarly journals Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer
Keyword(s):  
High Resolution ◽  
Crop Production ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Model ◽  
Stress Index ◽  
Yield Prediction ◽  
Field Scale ◽  
Crop Water Stress Index ◽  
Sentinel 2

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.

Calibration and validation of the Modified Universal Soil Loss Equation for estimating sediment yield on sloping plots: A case study in Khun Satan catchment of Northern Thailand

2010 ◽  
Vol 90 (4) ◽  
pp. 585-596 ◽  
Author(s):  
S. Pongsai ◽  
D. Schmidt Vogt ◽  
R.P. Shrestha ◽  
R.S. Clemente ◽  
A. Eiumnoh
Keyword(s):  
Sediment Yield ◽  
Soil Loss ◽  
The Other ◽  
Universal Soil Loss Equation ◽  
Model Parameters ◽  
Rainfall Events ◽  
Sediment Yields ◽  
Calibration And Validation ◽  
S Factor ◽  
Soil Loss Equation

In this study, model testing, calibration, and validation of the Modified Universal Soil Loss Equation (MUSLE) model were carried out in Khun Satan catchment, Thailand, for the estimation of sediment yield in plots of different slopes using the S factor from the classic Universal Soil Loss Equation (USLE) and the McCool model, as the calibration parameter. In situ experimental plots were established with five different inclinations (9, 16, 25, 30, and 35%), with the other model parameters (e.g., erodibility, conservation practice, etc) being treated as constants. Sediment yields were recorded from 27 rainfall events between July and October 2003. It was found that both the classic USLE and the McCool models over-estimated sediment yields at all slope angles. However, the classic USLE produced a smaller relative error (RE) than the McCool model at plots with slopes of 9 and 16%, while the McCool model performed better at plots with slopes over 16% inclination. The calibration of the model using the S factor was then made for two slope range intervals, and the slope algorithm was later modified. The calibrated S factors were used in the prototype model for slope ranges of 9 to 16% using classic USLE and for slopes from 16 to 35% using the McCool model. The results revealed that an acceptable accuracy can be obtained through model calibration. The model validation based on paired t-test, on the other hand, showed that there was no difference (α = 0.05) between measured and estimated sediment yield using both models. This result indicates that if data on various slope gradients are limited, MUSLE needs to be calibrated before application, especially with respect to topographic factors, in order to obtain an accurate estimate of the sediment yield from individual rainfall events.

Targeting BMP Placement using SWAT Sediment Yield Estimates for Field-Scale BMPs

2010 ◽  
Author(s):  
Prasad Daggupati ◽  
Kyle R Douglas-Mankin ◽  
Aleksey Y Sheshukov ◽  
Philip L Barnes
Keyword(s):  
Sediment Yield ◽  
Field Scale

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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