Evidence map›Paper›PMID 38778950›Full record

ArticleHeliyon2024

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data.

Sangchul Lee, Dongho Kim, Gregory W McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E Moglen, Xuesong Zhang, Haw Yen, Junyu Qi and 3 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Sangchul LeeDivision of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea.
Dongho KimDepartment of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of Korea.
Gregory W McCartyUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Martha AndersonUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Feng GaoUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Fangni LeiUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Glenn E MoglenDepartment of Civil and Environmental Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Xuesong ZhangUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Haw YenSchool of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA.
Junyu QiEarth System Science Interdisciplinary Center, University of Maryland, College Park, 5825 University Research Ct, College Park, MD 20740, USA.
Wade CrowUSDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
In-Young YeoSchool of Engineering, The University of Newcastle, Callaghan NSW 2308, Australia.
Liang SunKey Laboratory of Agricultural Remote Sensing, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Indexed as

Additional constraintsParameter uncertaintyRemotely sensed productsSoil and water assessment tool (SWAT)

Identifiers

PMID38778950
PMCPMC11108841

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.