Evidence map›Paper›PMID 39056051›Full record

ArticlePeerJ2024

Methodology for the assessment of poor-data water resources.

María Del Mar Navarro-Farfán, Liliana García-Romero, Marco A Martínez-Cinco, Constantino Domínguez-Sánchez, Sonia Tatiana Sánchez-Quispe

Abstract read
In one paragraph

Article in PeerJ, 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
–field-weighted citation impact
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

5 authors.

María Del Mar Navarro-FarfánFaculty of Chemical Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.
Liliana García-RomeroFaculty of Civil Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.
Marco A Martínez-CincoFaculty of Chemical Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.ORCID 0000-0001-8931-4169
Constantino Domínguez-SánchezFaculty of Civil Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.
Sonia Tatiana Sánchez-QuispeFaculty of Civil Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico.

Funding

National Council for Humanities, Science and Technology of Mexico (Conahcyt)
6 · The paper itself

Abstract

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Indexed as

Water ResourcesEnvironmental MonitoringHydrologyModels, TheoreticalData analysisMethodologyNested basinsPoor-dataSurface modeling

Identifiers

PMID39056051
PMCPMC11271652

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.