FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2958403742> ?p ?o ?g. }

• W2958403742 endingPage "123950" @default.
• W2958403742 startingPage "123950" @default.
• W2958403742 abstract "Environmental tracer data, such as the stable water isotopic composition of streamwater and precipitation, are valuable for understanding runoff generation processes and calibrating hydrological models. Despite recent technical advancements, the collection and analysis of streamwater and precipitation samples still involve significant costs and efforts. Consequently, it is useful to study how many samples need to be collected as a basis for model calibration and what the most informative sampling times are. In previous studies, we used the Birkenes hydrological model and synthetic data to explore the value of a few stream isotope samples for event-based model calibration. Our results showed that the information from two or three selected isotope samples, and particularly a sample taken during the falling limb of the hydrograph, improved model performance significantly compared to calibration against streamflow data only. In this follow-up study, we used a unique Swiss dataset with high-frequency isotope measurements of precipitation and streamwater during six rainfall-runoff events to determine which stream isotope samples were most informative for model calibration. Several benchmarks were used to judge model validation performance. Our results showed that for 83% of the 42 possible combinations of calibration and validation events two strategically selected streamwater samples improved model performance compared to the lower benchmark when only stream stage data were used for calibration. The most informative samples also improved the calibration of a mixing-related parameter. When the model was calibrated and validated for the same event, the most informative samples for model calibration were those from the early part of the falling limb, which confirmed the results of the previous studies with synthetic data. Pre-event isotope samples were more informative when the model was calibrated for one event and then validated against other events or all events combined. Our results suggest that relatively inexpensive event-based data for model calibration can be obtained for so-far ungauged catchments by placing a water level logger in the stream and collecting precipitation isotope data and a few streamflow samples. These samples should preferably include a pre-event sample and a sample from the early falling limb. However, similar analyses for a wider range of sites and events, in combination with a selection of models with different structures and assumptions, are needed to confirm these results and to optimise sampling strategies further." @default.
• W2958403742 created "2019-07-23" @default.
• W2958403742 creator A5012321099 @default.
• W2958403742 creator A5023484674 @default.
• W2958403742 creator A5028147456 @default.
• W2958403742 creator A5062252746 @default.
• W2958403742 creator A5090344707 @default.
• W2958403742 date "2019-10-01" @default.
• W2958403742 modified "2023-09-25" @default.
• W2958403742 title "What is the best time to take stream isotope samples for event-based model calibration?" @default.
• W2958403742 cites W1600664870 @default.
• W2958403742 cites W1895260358 @default.
• W2958403742 cites W1907546193 @default.
• W2958403742 cites W1972285234 @default.
• W2958403742 cites W1985204322 @default.
• W2958403742 cites W1993292702 @default.
• W2958403742 cites W1995320409 @default.
• W2958403742 cites W1999310382 @default.
• W2958403742 cites W2000272857 @default.
• W2958403742 cites W2002680866 @default.
• W2958403742 cites W2002859075 @default.
• W2958403742 cites W2023677451 @default.
• W2958403742 cites W2028069422 @default.
• W2958403742 cites W2033094743 @default.
• W2958403742 cites W2034616666 @default.
• W2958403742 cites W2057862075 @default.
• W2958403742 cites W2063582271 @default.
• W2958403742 cites W2072100520 @default.
• W2958403742 cites W2078483536 @default.
• W2958403742 cites W2088407427 @default.
• W2958403742 cites W2089969596 @default.
• W2958403742 cites W2095814372 @default.
• W2958403742 cites W2105765357 @default.
• W2958403742 cites W2115081203 @default.
• W2958403742 cites W2117718007 @default.
• W2958403742 cites W2125294001 @default.
• W2958403742 cites W2132201524 @default.
• W2958403742 cites W2133837084 @default.
• W2958403742 cites W2144367722 @default.
• W2958403742 cites W2145635192 @default.
• W2958403742 cites W2146865018 @default.
• W2958403742 cites W2152932346 @default.
• W2958403742 cites W2153929342 @default.
• W2958403742 cites W2157824523 @default.
• W2958403742 cites W2408447390 @default.
• W2958403742 cites W2557148279 @default.
• W2958403742 cites W2596769535 @default.
• W2958403742 cites W2612588898 @default.
• W2958403742 cites W2776947039 @default.
• W2958403742 cites W2791827404 @default.
• W2958403742 cites W2889344703 @default.
• W2958403742 doi "https://doi.org/10.1016/j.jhydrol.2019.123950" @default.
• W2958403742 hasPublicationYear "2019" @default.
• W2958403742 type Work @default.
• W2958403742 sameAs 2958403742 @default.
• W2958403742 citedByCount "8" @default.
• W2958403742 countsByYear W29584037422020 @default.
• W2958403742 countsByYear W29584037422021 @default.
• W2958403742 countsByYear W29584037422022 @default.
• W2958403742 countsByYear W29584037422023 @default.
• W2958403742 crossrefType "journal-article" @default.
• W2958403742 hasAuthorship W2958403742A5012321099 @default.
• W2958403742 hasAuthorship W2958403742A5023484674 @default.
• W2958403742 hasAuthorship W2958403742A5028147456 @default.
• W2958403742 hasAuthorship W2958403742A5062252746 @default.
• W2958403742 hasAuthorship W2958403742A5090344707 @default.
• W2958403742 hasBestOaLocation W29584037422 @default.
• W2958403742 hasConcept C105795698 @default.
• W2958403742 hasConcept C106131492 @default.
• W2958403742 hasConcept C107054158 @default.
• W2958403742 hasConcept C121332964 @default.
• W2958403742 hasConcept C126645576 @default.
• W2958403742 hasConcept C127313418 @default.
• W2958403742 hasConcept C140779682 @default.
• W2958403742 hasConcept C153294291 @default.
• W2958403742 hasConcept C154936535 @default.
• W2958403742 hasConcept C165838908 @default.
• W2958403742 hasConcept C185544564 @default.
• W2958403742 hasConcept C187320778 @default.
• W2958403742 hasConcept C18903297 @default.
• W2958403742 hasConcept C205649164 @default.
• W2958403742 hasConcept C2778863792 @default.
• W2958403742 hasConcept C31972630 @default.
• W2958403742 hasConcept C33923547 @default.
• W2958403742 hasConcept C39432304 @default.
• W2958403742 hasConcept C41008148 @default.
• W2958403742 hasConcept C50477045 @default.
• W2958403742 hasConcept C53739315 @default.
• W2958403742 hasConcept C58640448 @default.
• W2958403742 hasConcept C76886044 @default.
• W2958403742 hasConcept C86803240 @default.
• W2958403742 hasConceptScore W2958403742C105795698 @default.
• W2958403742 hasConceptScore W2958403742C106131492 @default.
• W2958403742 hasConceptScore W2958403742C107054158 @default.
• W2958403742 hasConceptScore W2958403742C121332964 @default.
• W2958403742 hasConceptScore W2958403742C126645576 @default.
• W2958403742 hasConceptScore W2958403742C127313418 @default.
• W2958403742 hasConceptScore W2958403742C140779682 @default.

• next