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Record W3144122406

Improvement of snowmelt implementation in the SWAT hydrologic model

2013· article· en· W3144122406 on OpenAlexaff
Wen-Ju Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsScience North
Fundersnot available
KeywordsSnowmeltEnvironmental scienceSurface runoffHydrology (agriculture)SnowSWAT modelMeltwaterGeologyMeteorologyGeographyEcology
DOInot available

Abstract

fetched live from OpenAlex

SWAT is a physically explicit distributed hydrologic model,which could simulate hydrological processes such as surface runoff,snowmelt runoff and infiltration with geographic information system( GIS). The surface runoff component in SWAT is implemented by using the SCS curve number and snowmelt is calculated by a relatively simple,empirical degree- day method. Those approaches work well in humid and semi-humid areas where precipitation dominantly controls runoff. In cold and arid regions,for example,in a case study in the Heihe river basin( HRB) of northwest China,however,it revealed those implementations cannot represent the effects of snowmelt in springs and thus impact surface runoff simulations. At large,snowmelt in such areas is underestimated and more discrepancies are consequently introduced to the overall simulation accuracy. The FASST model is a surface process model with explicit physical base,including a snowmelt runoff component that makes use of mass and energy balance equations. There is a snowmelt implementation in FASST, which takes topography,vegetation,soil type,and snow physical properties into account. Existing applications of FASST show good simulations of snowmelt in terrain-complex mountainous watershed and its applicability is also confirmed by an application to the Tangula site of northwest China located in a similar cold and alpine area. This paper proposes a coupling approach to improve the simulation of snowmelt by integrating FASST snowmelt to SWAT. In this approach,when the snowmelt begins to be calculated in SWAT,it will call FASST snowmelt to calculate,return its value to the SWAT corresponding variable and continue remainder SWAT logics. The technical implementation is presented in detail. An application to the upper mountainous HRB is set up to test its performance. There are abundant snow falls in Upper HRB in winters and snowmelt is the primary water source to river in springs that cannot be simulated well by original SWAT. The improvements in comparison to the original were examined from three aspects,namely,streamflow,snowmelt runoff and surface runoff contribution to streamflow. Both snowmelt and surface runoff estimates with the coupled model were increased so that streamflow estimate was more close to that observed. An improvement of monthly streamflow estimation by 0. 11 in Nash-Sutcliffe coefficient( NSE) can be achieved. Examination of surface runoff contribution to streamflow also supports its feasibility in estimating snowmelt by the coupled approach. Moreover,by using optimized parameter sets,the monthly streamflow simulation accuracy in the validation period of 2000—2009 can be up to 0. 83 in NSE. The results confirm the applicability of the SWAT-FASST coupled approach in cold and alpine watersheds where snowmelt should be taken into account and suggest its significance in improving the simulations in such areas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.239
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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