Quantifying the Contribution of Extreme Climate Indices to the Dynamics of Net Primary Productivity of Vegetation in the Qinba Mountain Area

[Objective] To thoroughly investigate the effects of extreme climate events on the net primary productivity (NPP) of vegetation in the Qinba Mountain area. [Methods] Using the Qinba Mountain area as an example area, 161 meteorological station observations from 1960 to 2023 and MOD17A3 dataset from 2001 to 2020 were used, and fitted regression, ridge regression and other analytical methods were applied to study the spatial and temporal evolution of the two and to quantify the contribution of extreme climate indices to the trend of NPP change. [Results] 1) the overall development of NPP in the study area was favourable, with an average annual growth rate of 5.02 g/(m2·a) (calculated by C). 2) Extreme warm events significantly increased and extreme cold events significantly decreased in the Qinba Mountain area from 1960 to 2023, whereas the changes in extreme precipitation events were not obvious. 3) Changes in the CSDI, ID, TN10p, CDD, R10, R20 and TR had a positive impact on the NPP trends, with the greatest contribution from the TN10p; changes in the Rx5day negatively affected the NPP trends. [Conclusion] The study can provide data or theoretical support for the conservation of terrestrial ecosystems in the Qinba Mountain area under the background of climate warming and for coping with extreme climate events.

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-04-01
DOI
https://doi.org/10.13870/j.cnki.stbcxb.2025.02.018
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantifying the Contribution of Extreme Climate Indices to the Dynamics of Net Primary Productivity of Vegetation in the Qinba Mountain Area

SHILAP Revista de lepidopterología
Remote Sensing in Agriculture
article

Quantifying the Contribution of Extreme Climate Indices to the Dynamics of Net Primary Productivity of Vegetation in the Qinba Mountain Area

article en

Abstract

[Objective] To thoroughly investigate the effects of extreme climate events on the net primary productivity (NPP) of vegetation in the Qinba Mountain area. [Methods] Using the Qinba Mountain area as an example area, 161 meteorological station observations from 1960 to 2023 and MOD17A3 dataset from 2001 to 2020 were used, and fitted regression, ridge regression and other analytical methods were applied to study the spatial and temporal evolution of the two and to quantify the contribution of extreme climate indices to the trend of NPP change. [Results] 1) the overall development of NPP in the study area was favourable, with an average annual growth rate of 5.02 g/(m2·a) (calculated by C). 2) Extreme warm events significantly increased and extreme cold events significantly decreased in the Qinba Mountain area from 1960 to 2023, whereas the changes in extreme precipitation events were not obvious. 3) Changes in the CSDI, ID, TN10p, CDD, R10, R20 and TR had a positive impact on the NPP trends, with the greatest contribution from the TN10p; changes in the Rx5day negatively affected the NPP trends. [Conclusion] The study can provide data or theoretical support for the conservation of terrestrial ecosystems in the Qinba Mountain area under the background of climate warming and for coping with extreme climate events.

SHILAP Revista de lepidopterología
Henan Polytechnic University (CN)
Climate action
Openalex Percentile: Top 57%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.