A novel bias correction method for extreme rainfall events based on L‐moments-Reference-Cited by-同舟云学术

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A novel bias correction method for extreme rainfall events based on L‐moments

Published:2021-06-17 Issue:1 Volume:42 Page:250-264
ISSN:0899-8418
Container-title:International Journal of Climatology
language:en
Short-container-title:Intl Journal of Climatology

Author:

Anthanahalli Nanjegowda Rohith¹^ORCID,Kulamulla Parambath Sudheer¹²³^ORCID

Affiliation:

1. Department of Civil Engineering Indian Institute of Technology Madras Chennai Tamil Nadu India

2. Kerala State Council for Science Technology and Environment Thiruvananthapuram Kerala India

3. Department of Agricultural and Biological Engineering Purdue University West Lafayette Indiana USA

Funder

Indian Institute of Technology Madras

Publisher

Wiley

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7242

Reference33 articles.

1. On the bias correction of general circulation model output for Indian summer monsoon

2. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

3. Comparison of Annual Maximum and Partial Duration Series for Derivation of Rainfall Intensity-Duration-Frequency Relationships in Peninsular Malaysia

4. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America

5. On bias correction of summer monsoon precipitation over India from CORDEX-SA simulations

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1. Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data;Stochastic Environmental Research and Risk Assessment;2024-06-25

2. An extremes-weighted empirical quantile mapping for global climate model data bias correction for improved emphasis on extremes;Theoretical and Applied Climatology;2024-04-10

3. Understanding future hydrologic challenges: Modelling the impact of climate change on river runoff in central Italy;Environmental Challenges;2024-04

4. A novel safe-fail framework for the design of urban stormwater drainage infrastructures with minimal failure and flood severity;Journal of Hydrology;2023-12

5. Investigating the Impact of the Spatiotemporal Bias Correction of Precipitation in CMIP6 Climate Models on Drought Assessments;Remote Sensing;2022-12-06

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