Matches in SemOpenAlex for { <https://semopenalex.org/work/W4294032018> ?p ?o ?g. }
- W4294032018 endingPage "6600" @default.
- W4294032018 startingPage "6581" @default.
- W4294032018 abstract "Abstract. Karst trough and valley landforms are prone to flooding, primarily because of the unique hydrogeological features of karst landforms, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting flooding are highly dependent on distributed modelling, complicated boundary parameter settings and extensive hydrogeological data processing, which consumes large amounts of both time and computational power. Proposed here is a distributed physically based karst hydrological model known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and fewer parameters are included in this model than in the current distributed models. This allows karst areas to be modelled with only a small amount of necessary hydrogeological data. Eighteen flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations: the average values of the Nash–Sutcliffe coefficient and the water balance coefficient are both 0.92, while the average relative flow process error is 10 % and the flood peak error is 11 %. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding enabled by the proposed QMG model promotes a better mechanistic depiction of runoff generation and confluence in karst trough valleys." @default.
- W4294032018 created "2022-09-01" @default.
- W4294032018 creator A5001940547 @default.
- W4294032018 creator A5020240685 @default.
- W4294032018 creator A5040780405 @default.
- W4294032018 creator A5042876848 @default.
- W4294032018 creator A5081162222 @default.
- W4294032018 date "2022-09-01" @default.
- W4294032018 modified "2023-09-30" @default.
- W4294032018 title "A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations" @default.
- W4294032018 cites W138450903 @default.
- W4294032018 cites W1631009898 @default.
- W4294032018 cites W1651470089 @default.
- W4294032018 cites W1827740926 @default.
- W4294032018 cites W1884508336 @default.
- W4294032018 cites W1965568221 @default.
- W4294032018 cites W1968439412 @default.
- W4294032018 cites W1970387917 @default.
- W4294032018 cites W1980238813 @default.
- W4294032018 cites W1993069325 @default.
- W4294032018 cites W1993693084 @default.
- W4294032018 cites W2013435447 @default.
- W4294032018 cites W2013871756 @default.
- W4294032018 cites W2020732968 @default.
- W4294032018 cites W2025523721 @default.
- W4294032018 cites W2033038717 @default.
- W4294032018 cites W2033993157 @default.
- W4294032018 cites W2037340892 @default.
- W4294032018 cites W2039996318 @default.
- W4294032018 cites W2041795629 @default.
- W4294032018 cites W2044308175 @default.
- W4294032018 cites W2046861252 @default.
- W4294032018 cites W2048296563 @default.
- W4294032018 cites W2050573433 @default.
- W4294032018 cites W2054046647 @default.
- W4294032018 cites W2065286385 @default.
- W4294032018 cites W2065964458 @default.
- W4294032018 cites W2075108083 @default.
- W4294032018 cites W2081999940 @default.
- W4294032018 cites W2108716476 @default.
- W4294032018 cites W2114526358 @default.
- W4294032018 cites W2124738823 @default.
- W4294032018 cites W2139545475 @default.
- W4294032018 cites W2144237274 @default.
- W4294032018 cites W2144734504 @default.
- W4294032018 cites W2500977297 @default.
- W4294032018 cites W2525872179 @default.
- W4294032018 cites W2545631585 @default.
- W4294032018 cites W2744690766 @default.
- W4294032018 cites W2766782102 @default.
- W4294032018 cites W2774759767 @default.
- W4294032018 cites W2778746049 @default.
- W4294032018 cites W2910672219 @default.
- W4294032018 cites W2922378428 @default.
- W4294032018 cites W2965612263 @default.
- W4294032018 cites W2991331425 @default.
- W4294032018 cites W3025603304 @default.
- W4294032018 cites W3047533223 @default.
- W4294032018 cites W3093346785 @default.
- W4294032018 cites W3095648360 @default.
- W4294032018 cites W3126230884 @default.
- W4294032018 cites W3156464629 @default.
- W4294032018 cites W3184255623 @default.
- W4294032018 cites W3186220791 @default.
- W4294032018 cites W3215458773 @default.
- W4294032018 cites W4205542030 @default.
- W4294032018 doi "https://doi.org/10.5194/gmd-15-6581-2022" @default.
- W4294032018 hasPublicationYear "2022" @default.
- W4294032018 type Work @default.
- W4294032018 citedByCount "0" @default.
- W4294032018 crossrefType "journal-article" @default.
- W4294032018 hasAuthorship W4294032018A5001940547 @default.
- W4294032018 hasAuthorship W4294032018A5020240685 @default.
- W4294032018 hasAuthorship W4294032018A5040780405 @default.
- W4294032018 hasAuthorship W4294032018A5042876848 @default.
- W4294032018 hasAuthorship W4294032018A5081162222 @default.
- W4294032018 hasBestOaLocation W42940320181 @default.
- W4294032018 hasConcept C108497213 @default.
- W4294032018 hasConcept C114793014 @default.
- W4294032018 hasConcept C121332964 @default.
- W4294032018 hasConcept C126197015 @default.
- W4294032018 hasConcept C127313418 @default.
- W4294032018 hasConcept C138885662 @default.
- W4294032018 hasConcept C151730666 @default.
- W4294032018 hasConcept C153294291 @default.
- W4294032018 hasConcept C153400128 @default.
- W4294032018 hasConcept C159390177 @default.
- W4294032018 hasConcept C182348080 @default.
- W4294032018 hasConcept C187320778 @default.
- W4294032018 hasConcept C18903297 @default.
- W4294032018 hasConcept C27206212 @default.
- W4294032018 hasConcept C33556824 @default.
- W4294032018 hasConcept C39432304 @default.
- W4294032018 hasConcept C49204034 @default.
- W4294032018 hasConcept C50477045 @default.
- W4294032018 hasConcept C74256435 @default.
- W4294032018 hasConcept C76886044 @default.
- W4294032018 hasConcept C86803240 @default.