Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

Khem Chunpanha; Yan Baowen

doi:doi:10.11648/j.wros.20241302.13

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Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

Khem Chunpanha

, Yan Baowen^*

Published in Journal of Water Resources and Ocean Science (Volume 13, Issue 2)

Received: 16 May 2024 Accepted: 6 June 2024 Published: 19 June 2024

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Abstract

The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95^th percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R²>0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

Published in	Journal of Water Resources and Ocean Science (Volume 13, Issue 2)
DOI	10.11648/j.wros.20241302.13
Page(s)	55-62
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Mountain Equivalent Rainfall, Extreme Precipitation, TRMM, Validation, Qinba Mountain Area

References

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APA Style

Chunpanha, K., Baowen, Y. (2024). Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. Journal of Water Resources and Ocean Science, 13(2), 55-62. https://doi.org/10.11648/j.wros.20241302.13

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ACS Style

Chunpanha, K.; Baowen, Y. Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. J. Water Resour. Ocean Sci. 2024, 13(2), 55-62. doi: 10.11648/j.wros.20241302.13

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AMA Style

Chunpanha K, Baowen Y. Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. J Water Resour Ocean Sci. 2024;13(2):55-62. doi: 10.11648/j.wros.20241302.13

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@article{10.11648/j.wros.20241302.13,
  author = {Khem Chunpanha and Yan Baowen},
  title = {Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM
},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {13},
  number = {2},
  pages = {55-62},
  doi = {10.11648/j.wros.20241302.13},
  url = {https://doi.org/10.11648/j.wros.20241302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20241302.13},
  abstract = {The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95th percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R2 >0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.
},
 year = {2024}
}

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TY  - JOUR
T1  - Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

AU  - Khem Chunpanha
AU  - Yan Baowen
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.wros.20241302.13
DO  - 10.11648/j.wros.20241302.13
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 55
EP  - 62
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20241302.13
AB  - The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95th percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R2 >0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

VL  - 13
IS  - 2
ER  -

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Author Information

Khem Chunpanha

College of Water Conservancy and Architectural Engineering, Northwest A&F University, Yangling, China

Contact Email

http://orcid.org/0009-0001-4504-8441
Yan Baowen

College of Water Conservancy and Architectural Engineering, Northwest A&F University, Yangling, China

Contact Email

http://orcid.org/0000-0002-6039-115X

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Plain Text BibTeX RIS

APA Style

Chunpanha, K., Baowen, Y. (2024). Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. Journal of Water Resources and Ocean Science, 13(2), 55-62. https://doi.org/10.11648/j.wros.20241302.13

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ACS Style

Chunpanha, K.; Baowen, Y. Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. J. Water Resour. Ocean Sci. 2024, 13(2), 55-62. doi: 10.11648/j.wros.20241302.13

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AMA Style

Chunpanha K, Baowen Y. Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM. J Water Resour Ocean Sci. 2024;13(2):55-62. doi: 10.11648/j.wros.20241302.13

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@article{10.11648/j.wros.20241302.13,
  author = {Khem Chunpanha and Yan Baowen},
  title = {Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM
},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {13},
  number = {2},
  pages = {55-62},
  doi = {10.11648/j.wros.20241302.13},
  url = {https://doi.org/10.11648/j.wros.20241302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20241302.13},
  abstract = {The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95th percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R2 >0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

AU  - Khem Chunpanha
AU  - Yan Baowen
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.wros.20241302.13
DO  - 10.11648/j.wros.20241302.13
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 55
EP  - 62
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20241302.13
AB  - The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95th percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R2 >0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

VL  - 13
IS  - 2
ER  -

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