Page 98 - 《高原气象》2025年第3期

高 原 气 象 44 卷
656

The Spatiotemporal Variations Characteristics of Precipitation in the

Ailao Mountain Area during 2000 -2020 based on Geographical
Weighted Regression Downscaling Method

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5
2， 3
WANG Hong ， YAN Qiaoshun ， ZHAO Zujun ， CHEN Daxiang ， ZHANG Zhiming 1
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（1. Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China， School of Ecology and Environmental
Science， Yunnan University， Kunming 650500， Yunan， China；
2. Ailaoshan Station of Subtropical Forest Ecosystem Studies， Xishuangbanna Tropical Botanical Garden， Chinese Academy of
Sciences， Jingdong 676209， Yunnan， China；
3. Ailaoshan Subtropical Forest Ecosystem Observation and Research Station of Yunnan Province， Jingdong 676200， Yunnan， China；
4. Yunnan Ecological and Environmental Monitoring Center， Kunming 650500， Yunan， China；
5. Tongbiguan Provincial Nature Reserve， Mangshi 678499， Yunan， China）

Abstract： High-quality precipitation is an important precondition to conduct the study of ecohydrology and cli‐
mate change in mountain area. However， complicated terrain and scarce and uneven ground observation stations
make the understanding of spatiotemporal variations characteristics of precipitation in the Ailao Mountain Area
unclear. In this study， GWR （Geographical Weighted Regression） model is used to downscale GSMaP-Gauge
precipitation data with 0. 1° spatial resolution to 30 m. After validating the accuracy of downscaled precipitation
data by using monthly meteorological stations data， the monthly precipitation dataset from 2000 to 2020 in the
Ailao Mountain Area is developed. Based on this dataset， the long-term （2000 -2020） spatiotemporal variations
characteristics of precipitation at both annual and monthly scales in the study area are illustrated. The results
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showed that （1） the accuracy of downscaled GSMaP-Gauge precipitation by GWR model was reliable （R =
0. 77， Bias=-0. 01）， with the significant improvement of spatial details.（2） Spatially， the annual precipitation
amount increased from north to south， and it first increased and then decreased with the rising of elevation in the
Ailao Mountain Area. Temporally， there was obvious dry and wet seasons in the study area from 2000 to 2020，
with the precipitation amount from May to September accounting for 74. 74% of the annual precipitation. And
precipitation amount first increased and then decreased from February to December， with the maximum occurred
in July.（3） From the aspect of spatiotemporal change， the annual average precipitation amount decreased from
2001 to 2020， only 24. 19% area was at an increasing trend concentrated at the southeastern Ailao Mountain Ar‐
ea. As for different months， precipitation amount was significantly increased in January and significantly de‐
creased in May， while the change trends of precipitation in the other months were insignificant. This study has
found that GWR downscaling method is valid to obtain high-resolution precipitation data， which is also an effec‐
tive pathway to clarify the spatiotemporal characteristics of precipitation， and to provide key and basic data for
ecohydrological process study and regional water resources management in mountain area.
Key words： geographical weighted regression； precipitation amount； downscaling； spatiotemporal change；
complicated mountain area