Enhancing Rainfall Data Consistency and Completeness: A Spatiotemporal Quality Control Approach and Missing Data Reconstruction Using MICE on Large Precipitation Datasets-Reference-Cited by-同舟云学术

Enhancing Rainfall Data Consistency and Completeness: A Spatiotemporal Quality Control Approach and Missing Data Reconstruction Using MICE on Large Precipitation Datasets

Published:2024-01-22 Issue:3 Volume:38 Page:815-833
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Golkhatmi Nafiseh Seyyed Nezhad^ORCID,Farzandi Mahboobeh^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11269-023-03567-0.pdf

Reference19 articles.

1. Aieb A, Madani K, Scarpa M, Bonaccorso B, Lefsih K (2019) A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed. Algeria. Heliyon 5(2):e01247. https://doi.org/10.1016/j.heliyon.2019.e01247

2. Barrios A, Trincado G, Garreaud R (2018) Alternative approaches for estimating missing climate data: Application to monthly precipitation records in South-Central Chile. Forest Ecosyst 5(1):1–10. https://doi.org/10.1186/s40663-018-0147-x

3. Daly C, Gibson W, Doggett M, Smith J, Taylor G (2004) A probabilistic-spatial approach to the quality control of climate observations. Proc AMS Conf Appl Climatol Am Meteorol Soc, Seattle, WA

4. Estévez J, Llabrés-Brustenga A, Casas-Castillo MC, García-Marín AP, Kirchner R, Rodríguez-Solà R (2022) A quality control procedure for long-term series of daily precipitation data in a semiarid environment. Theoret Appl Climatol 149(3–4):1029–1041

5. Farzandi M, Sanaeinejad H, Rezaei-Pazhan H, Sarmad M (2021) Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area. Environ Dev Sustain 1–20. https://doi.org/10.1007/s10668-021-01784-4