FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4360616431> ?p ?o ?g. }

• W4360616431 endingPage "1250" @default.
• W4360616431 startingPage "1250" @default.
• W4360616431 abstract "Quantitatively analyzing models’ uncertainty is essential for agricultural models due to the effect of inputs parameters and processes on increasing models’ uncertainties. The main aim of the current study was to explore the influence of input parameter uncertainty on the output of the well-known surface irrigation software model of WinSRFR. The generalized likelihood uncertainty estimation (GLUE) framework was used to explicitly evaluate the uncertainty model of WinSRFR. The epistemic uncertainties of WinSRFR furrow irrigation simulations, including the advance front curve, flow depth hydrograph, and runoff hydrograph, were assessed in response to change key input parameters related to the Kostiakov–Lewis infiltration function, Manning’s roughness coefficient, and the geometry cross section. Three likelihood measures of Nash–Sutcliffe efficiency (NSE), percentage bias (PBIAS), and the coefficient of determination (R2) were used in GLUE analysis for selecting behavioral estimations of the model outputs. The uncertainty of the WinSRFR model was investigated under two furrow irrigation system conditions, closed end and open end. The results showed the likelihood measures considerably influence the width of uncertainty bounds. WinSRFR outputs have high uncertainty for cross section parameters relative to soil infiltration and roughness parameters. Parameters of soil infiltration and roughness coefficient play an important role in reducing the uncertainty bound width and number of observations, especially by filtering non-behavioral data using likelihood measures. The simulation errors of advance front curve and runoff hydrograph outputs with a PBIAS function were relatively lower and stable compared with other those of the likelihood functions. The 95% prediction uncertainties (95PPU) of the advance front curve were calculated to be 87.5% in both close-ended and open-ended conditions whereas, it was 91.18% for the runoff hydrograph in the open-ended condition. Additionally, the NSE likelihood function more explicitly determined the uncertainty related to flow depth hydrograph estimations. The outputs of the model showed more uncertainty and instability in response to variability in soil infiltration parameters than the roughness coefficient did. Therefore, applying accurate field methods and equipment and proper measurements of soil infiltration is recommended. The results highlight the importance of accurately monitoring and determining model input parameters to access a suitable level of WinSRFR uncertainty. In conclusion, considering and analyzing the uncertainty of input parameters of WinSRFR models is critical and could provide a reference to obtain realistic and stable furrow irrigation simulations." @default.
• W4360616431 created "2023-03-24" @default.
• W4360616431 creator A5014176707 @default.
• W4360616431 creator A5030289864 @default.
• W4360616431 creator A5072746309 @default.
• W4360616431 creator A5083877664 @default.
• W4360616431 date "2023-03-22" @default.
• W4360616431 modified "2023-09-26" @default.
• W4360616431 title "Uncertainty Assessment of WinSRFR Furrow Irrigation Simulation Model Using the GLUE Framework under Variability in Geometry Cross Section, Infiltration, and Roughness Parameters" @default.
• W4360616431 cites W104742960 @default.
• W4360616431 cites W1511398556 @default.
• W4360616431 cites W1795027507 @default.
• W4360616431 cites W1871141884 @default.
• W4360616431 cites W1965678241 @default.
• W4360616431 cites W1967059058 @default.
• W4360616431 cites W1975392447 @default.
• W4360616431 cites W1985479415 @default.
• W4360616431 cites W1994083916 @default.
• W4360616431 cites W1998890631 @default.
• W4360616431 cites W2007927647 @default.
• W4360616431 cites W2014730309 @default.
• W4360616431 cites W2015882231 @default.
• W4360616431 cites W2040118474 @default.
• W4360616431 cites W2059092087 @default.
• W4360616431 cites W2064048664 @default.
• W4360616431 cites W2067372734 @default.
• W4360616431 cites W2075965726 @default.
• W4360616431 cites W2076350636 @default.
• W4360616431 cites W2076928780 @default.
• W4360616431 cites W2084949396 @default.
• W4360616431 cites W2087606093 @default.
• W4360616431 cites W2088816244 @default.
• W4360616431 cites W2091840862 @default.
• W4360616431 cites W2111467274 @default.
• W4360616431 cites W2120348445 @default.
• W4360616431 cites W2124738823 @default.
• W4360616431 cites W2141552141 @default.
• W4360616431 cites W2151035094 @default.
• W4360616431 cites W2159199314 @default.
• W4360616431 cites W2218665055 @default.
• W4360616431 cites W2594906534 @default.
• W4360616431 cites W2735867402 @default.
• W4360616431 cites W2737609272 @default.
• W4360616431 cites W2793945017 @default.
• W4360616431 cites W2800707782 @default.
• W4360616431 cites W2889369449 @default.
• W4360616431 cites W2895236356 @default.
• W4360616431 cites W2897189296 @default.
• W4360616431 cites W2910489728 @default.
• W4360616431 cites W2913213891 @default.
• W4360616431 cites W2924139945 @default.
• W4360616431 cites W2943053029 @default.
• W4360616431 cites W2985290323 @default.
• W4360616431 cites W2998305396 @default.
• W4360616431 cites W3004099869 @default.
• W4360616431 cites W3013122948 @default.
• W4360616431 cites W3023207182 @default.
• W4360616431 cites W3040726294 @default.
• W4360616431 cites W3095360678 @default.
• W4360616431 cites W3166893749 @default.
• W4360616431 cites W3173457889 @default.
• W4360616431 cites W3178153218 @default.
• W4360616431 doi "https://doi.org/10.3390/w15061250" @default.
• W4360616431 hasPublicationYear "2023" @default.
• W4360616431 type Work @default.
• W4360616431 citedByCount "0" @default.
• W4360616431 crossrefType "journal-article" @default.
• W4360616431 hasAuthorship W4360616431A5014176707 @default.
• W4360616431 hasAuthorship W4360616431A5030289864 @default.
• W4360616431 hasAuthorship W4360616431A5072746309 @default.
• W4360616431 hasAuthorship W4360616431A5083877664 @default.
• W4360616431 hasBestOaLocation W43606164311 @default.
• W4360616431 hasConcept C105795698 @default.
• W4360616431 hasConcept C121332964 @default.
• W4360616431 hasConcept C127313418 @default.
• W4360616431 hasConcept C153294291 @default.
• W4360616431 hasConcept C153400128 @default.
• W4360616431 hasConcept C154936535 @default.
• W4360616431 hasConcept C159390177 @default.
• W4360616431 hasConcept C159985019 @default.
• W4360616431 hasConcept C177803969 @default.
• W4360616431 hasConcept C187320778 @default.
• W4360616431 hasConcept C18903297 @default.
• W4360616431 hasConcept C192562407 @default.
• W4360616431 hasConcept C2779937294 @default.
• W4360616431 hasConcept C33923547 @default.
• W4360616431 hasConcept C39432304 @default.
• W4360616431 hasConcept C41478065 @default.
• W4360616431 hasConcept C50477045 @default.
• W4360616431 hasConcept C76886044 @default.
• W4360616431 hasConcept C86803240 @default.
• W4360616431 hasConcept C88862950 @default.
• W4360616431 hasConceptScore W4360616431C105795698 @default.
• W4360616431 hasConceptScore W4360616431C121332964 @default.
• W4360616431 hasConceptScore W4360616431C127313418 @default.
• W4360616431 hasConceptScore W4360616431C153294291 @default.
• W4360616431 hasConceptScore W4360616431C153400128 @default.
• W4360616431 hasConceptScore W4360616431C154936535 @default.

• next