FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2023474702> ?p ?o ?g. }

• W2023474702 endingPage "372" @default.
• W2023474702 startingPage "372" @default.
• W2023474702 abstract "Modelling complex vadose-zone hydrological processes across a range of spatial scales requires models and hydraulic properties commensurate with the scale of investigation. This study investigates the ability of two conceptual models with contrasting complexity and parameter requirements to quantify accurately the one-dimensional water balance in a soil–vegetation–atmosphere (SVA) system. The two models tested were: (i) the mechanistic HYDRUS-1D model, which numerically solves the Richards equation for saturated–unsaturated water flow; and (ii) a compartment or budget model that includes simplified schemes for redistribution of water in the soil. We discuss model performance for parameter sets obtained by inverse modelling for an SVA system developed in a podzol soil with Scots pine vegetation in Belgium. Soil hydraulic properties were derived from field-based soil water content data collected at multiple depths in two lysimeters installed in the multi-layered forest soil and subject to atmospheric boundary conditions during nearly one full hydrological year. Parameter optimisation was based on a genetic algorithm including elitism as an operator for improving the search for optimal solutions with better performance scores. Four scenarios were developed to investigate (i) the impact of the type of conceptual flow model (mechanistic or compartment), and (ii) the effect of the degree of detail or granularity used to describe the soil profile, on the accuracy of inverse modelling (i.e. five or two material layers with different hydraulic properties or a homogeneous profile with effective properties). Results showed that for models with the same number of material layers as the number of pedogenic horizons in the soil profile, both conceptual models reasonably match the observed water contents at all depths. The mechanistic model implemented in HYDRUS-1D was the more accurate with root mean-square error (RMSE) values for water content based on all data ~0.02 cm3 cm–3, whereas for the compartment model the RMSE was ~0.03 cm3 cm–3. The results further illustrated that for a mildly heterogeneous soil (in terms of coefficient of variation for estimated hydraulic properties between soil horizons), the five-layer soil profile could be replaced by a single set of effective hydraulic properties with only a 35% reduction in performance compared with the five-layer mechanistic model. A functional evaluation of model performance using the cumulative annual drainage revealed overall good performance of the simplified models; drainage values calculated with the five-layer compartment model and the one- and two-layer mechanistic model were never more than 36% larger than their reference value. Global inverse parameter optimisation routines such as the genetic algorithm applied here are powerful tools to determine field-scale hydraulic properties of heterogeneous soil profiles for simple and complex models; model and parameter complexity can be customised depending on data availability and computational constraints." @default.
• W2023474702 created "2016-06-24" @default.
• W2023474702 creator A5006396436 @default.
• W2023474702 creator A5036826224 @default.
• W2023474702 creator A5081433124 @default.
• W2023474702 date "2013-01-01" @default.
• W2023474702 modified "2023-09-25" @default.
• W2023474702 title "Inverse modelling with a genetic algorithm to derive hydraulic properties of a multi-layered forest soil" @default.
• W2023474702 cites W1544682161 @default.
• W2023474702 cites W1700119093 @default.
• W2023474702 cites W1965901514 @default.
• W2023474702 cites W1969782385 @default.
• W2023474702 cites W1971236297 @default.
• W2023474702 cites W1973677933 @default.
• W2023474702 cites W1980965960 @default.
• W2023474702 cites W1990236303 @default.
• W2023474702 cites W1991496765 @default.
• W2023474702 cites W1996650025 @default.
• W2023474702 cites W1996722300 @default.
• W2023474702 cites W2006420515 @default.
• W2023474702 cites W2006468366 @default.
• W2023474702 cites W2007792212 @default.
• W2023474702 cites W2013219125 @default.
• W2023474702 cites W2017787065 @default.
• W2023474702 cites W2020783104 @default.
• W2023474702 cites W2032600630 @default.
• W2023474702 cites W2039344716 @default.
• W2023474702 cites W2040346339 @default.
• W2023474702 cites W2041691547 @default.
• W2023474702 cites W2041801215 @default.
• W2023474702 cites W2043277559 @default.
• W2023474702 cites W2045427006 @default.
• W2023474702 cites W2048568378 @default.
• W2023474702 cites W2050619557 @default.
• W2023474702 cites W2056801090 @default.
• W2023474702 cites W2067049079 @default.
• W2023474702 cites W2067367159 @default.
• W2023474702 cites W2068244793 @default.
• W2023474702 cites W2068293753 @default.
• W2023474702 cites W2070483298 @default.
• W2023474702 cites W2074869363 @default.
• W2023474702 cites W2074882890 @default.
• W2023474702 cites W2080146244 @default.
• W2023474702 cites W2081969554 @default.
• W2023474702 cites W2087070363 @default.
• W2023474702 cites W2093289918 @default.
• W2023474702 cites W2101473977 @default.
• W2023474702 cites W2121791546 @default.
• W2023474702 cites W2133346604 @default.
• W2023474702 cites W2135534403 @default.
• W2023474702 cites W2138575452 @default.
• W2023474702 cites W2142635246 @default.
• W2023474702 cites W2160680742 @default.
• W2023474702 cites W2162604832 @default.
• W2023474702 cites W2168388823 @default.
• W2023474702 cites W2316778777 @default.
• W2023474702 cites W999207820 @default.
• W2023474702 doi "https://doi.org/10.1071/sr13144" @default.
• W2023474702 hasPublicationYear "2013" @default.
• W2023474702 type Work @default.
• W2023474702 sameAs 2023474702 @default.
• W2023474702 citedByCount "15" @default.
• W2023474702 countsByYear W20234747022016 @default.
• W2023474702 countsByYear W20234747022017 @default.
• W2023474702 countsByYear W20234747022018 @default.
• W2023474702 countsByYear W20234747022019 @default.
• W2023474702 countsByYear W20234747022020 @default.
• W2023474702 countsByYear W20234747022021 @default.
• W2023474702 countsByYear W20234747022022 @default.
• W2023474702 countsByYear W20234747022023 @default.
• W2023474702 crossrefType "journal-article" @default.
• W2023474702 hasAuthorship W2023474702A5006396436 @default.
• W2023474702 hasAuthorship W2023474702A5036826224 @default.
• W2023474702 hasAuthorship W2023474702A5081433124 @default.
• W2023474702 hasConcept C103272765 @default.
• W2023474702 hasConcept C11413529 @default.
• W2023474702 hasConcept C127313418 @default.
• W2023474702 hasConcept C156634047 @default.
• W2023474702 hasConcept C159390177 @default.
• W2023474702 hasConcept C159750122 @default.
• W2023474702 hasConcept C164285268 @default.
• W2023474702 hasConcept C187320778 @default.
• W2023474702 hasConcept C33923547 @default.
• W2023474702 hasConcept C35588792 @default.
• W2023474702 hasConcept C39432304 @default.
• W2023474702 hasConcept C63184880 @default.
• W2023474702 hasConcept C76886044 @default.
• W2023474702 hasConceptScore W2023474702C103272765 @default.
• W2023474702 hasConceptScore W2023474702C11413529 @default.
• W2023474702 hasConceptScore W2023474702C127313418 @default.
• W2023474702 hasConceptScore W2023474702C156634047 @default.
• W2023474702 hasConceptScore W2023474702C159390177 @default.
• W2023474702 hasConceptScore W2023474702C159750122 @default.
• W2023474702 hasConceptScore W2023474702C164285268 @default.
• W2023474702 hasConceptScore W2023474702C187320778 @default.
• W2023474702 hasConceptScore W2023474702C33923547 @default.
• W2023474702 hasConceptScore W2023474702C35588792 @default.
• W2023474702 hasConceptScore W2023474702C39432304 @default.

• next