Statistical Assessment of a Numerical Model Simulating Agro Hydrochemical Processes in Soil under Drip Fertigated Mandarin Tree

Skewes, Phogat V

doi:10.4172/2168-9768.1000155

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…R 2 of 0.66 and 0.64, and E = 0.34 and 0.34, respectively) during calibration (2009-2013) and validation (2013-2017) period. These values fell within the R 2 values (0.35-0.84) reported in other modelling studies (e.g., Coffey et al, 2004;Phogat et al, 2016). Other statistical estimates (ME, MAE, RMSE and SD; see Fig.…”

Section: Calibration and Validation Of Hydrus-2dsupporting

confidence: 89%

Optimizing the riparian zone width near a river for controlling lateral migration of irrigation water and solutes

Phogat

Cox

Kookana

et al. 2019

Journal of Hydrology

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Riparian zones are essential to preserve water quality of rivers adjacent to large areas of irrigated agriculture. We used HYDRUS (2D/3D) to quantify the long-term (8 years) influence of crops (almonds, wine grapes and potatocarrot) irrigated with recycled water on water and solute exchange at the Gawler River interface in relation to vegetation buffer widths from 10 to 110 m. We found that under almond and annual horticulture the likely average annual water flow from the irrigated area to the river was nearly twice as much (2.1 and 1.8, respectively) that under wine grapes. The hydraulic exchange at river interface for different irrigated crops was found to be sensitive to the buffer widths. For wine grapes, almonds and annual horticulture, the average annual hydraulic balance reached an equilibrium at 20, 65 and 55 m buffer widths, respectively. Furthermore, for wine grapes, with a 20 m buffer width, the average annual load of salts became negligible. This study shows that buffer widths of 20, 60, and 40 m for irrigated wine grapes, almond, and annual horticulture, respectively, are needed to restrict the migration of salts to the river. Further refinements are possible by incorporating the influence of preferential flow paths, improved water stress response functions, and addressing the data limitations for calibration of the model for solute dynamics.

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Section: Calibration and Validation Of Hydrus-2dsupporting

confidence: 89%

Optimizing the riparian zone width near a river for controlling lateral migration of irrigation water and solutes

Phogat

Cox

Kookana

et al. 2019

Journal of Hydrology

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“…After correcting the data model, the model accuracy test was conducted. Phogat et al, 2016 stated that the assessment of model performance is essential because reliable statistical comparison of observed data with simulated behavior of a model reflects the performance and consistency. In this study, the accuracy of the model was tested with three statistical parameters namely mean square error, R squared, and Nash-Sutcliffe Efficiency.…”

Section: Analysis Of Rcm Performance Before and After Bias Correctionmentioning

confidence: 99%

Comparison Performance of the Multi-Regional Climate Model (RCM) in Simulating Rainfall and Air Temperature in Batanghari Watershed

Handoko

Boer

Aldrian

et al. 2019

Aceh Int. J. Sci. Technol

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Many scientists assume that RCM output is directly used as input for climate change impact models, while it consists of systematic errors. Consequently, RCM still requires bias correction to be used as an input model. The purpose of this study was to analyze the RCM performance before and after bias correction, its best performance from several models, as well as to clarify the importance of bias correction before it is used to analyze climate change. As a result of this, the method used for bias correction was Distribution Mapping method (for rainfall) and Average Ratio-method (for air temperature). While the Generalized Extrem Valuedistribution (GEV) was used to analysis extreme rainfall. To determine the performance of the model before and after bias correction, statistical analysis was used namelyR2, NSE, and RMSE. Furthermore, ranking for every single model and Taylor Diagram was used to determine the best model. The results showed that the RCMs performance improved with bias correction. However, CSIRO-Mk3-6-0, CCSM4, GFDL-ESM2M, and MPI-ESM-MR models can be ignored as ensemble models, because they demonstrated poor performance in simulating rainfall. From this study, it was suggested that the best model in simulating daily and monthly rainfall was ACCESS1-0, while MIROC-ESM-CHEM (daily air temperature) and ACCESS1-0 (monthly air temperature) were best models used in simulating air temperature. Key words: RCM, bias correction, performance, rainfall, air temperature

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“…Bias correction using the average ratio method [ 18 ] was carried out by using the ratio of the average monthly rainfall of the observation data

to the average monthly rainfall of the NMME data

for each model according to equation ( 1 ).

where P obs is rainfall observation and P model is NMME rainfall forecast.…”

Section: Methodsmentioning

confidence: 99%

“…After correcting the NMME model, the model accuracy needs to be assessed. Phogat et al [ 18 ] stated that the evaluation of bias correction results is particularly important to determine the performance of the NMME model before and after correction. In this study, the models' accuracy is assessed with R squared ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE).…”

Section: Methodsmentioning

confidence: 99%

Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia

Faidah

Kuswanto

Sutikno

2022

The Scientific World Journal

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Generating an accurate rainfall prediction is a challenging work due to the complexity of the climate system. Numerous efforts have been conducted to generate reliable prediction such as through ensemble forecasts, the North Multi-Model Ensemble (NMME). The performance of NMME globally has been investigated in many studies. However, its performance in a specific location has not been much validated. This paper investigates the performance of NMME to forecast rainfall in Surabaya, Indonesia. Our study showed that the rainfall prediction from NMME tends to be underdispersive, which thus requires a bias correction. We proposed a new bias correction method based on gamma regression to model the asymmetric pattern of rainfall distribution and further compared the results with the average ratio method and linear regression. This study showed that the NMME performance can be improved significantly after bias correction using the gamma regression method. This can be seen from the smaller RMSE and MAE values, as well as higher R2 values compared with the results from linear regression and average ratio methods. Gamma regression improved the R2 value by about 30% higher than raw data, and it is about 20% higher than the linear regression approach. This research showed that NMME can be used to improve the accuracy of rainfall forecast in Surabaya.

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Statistical Assessment of a Numerical Model Simulating Agro Hydrochemical Processes in Soil under Drip Fertigated Mandarin Tree

Cited by 7 publications

References 26 publications

Optimizing the riparian zone width near a river for controlling lateral migration of irrigation water and solutes

Optimizing the riparian zone width near a river for controlling lateral migration of irrigation water and solutes

Comparison Performance of the Multi-Regional Climate Model (RCM) in Simulating Rainfall and Air Temperature in Batanghari Watershed

Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia

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