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Aiming at the limitation of real/near-real-time GNSS PWV retrieval under missing meteorological parameters, three PWV estimation models without the need for measured meteorological parameters were established in the Guangxi region based on the XGBoost model. First, the XGBZ-PWV model was developed with inputs including station time (DOY and HOD), location (Longitude, Latitude, and Height), and GNSS ZTD, and the output feature being GNSS PWV. Then, based on the XGBZ-PWV model, two empirical PWV values were incorporated to establish the XGBZG-PWV and XGBZE-PWV models, respectively. For comparison, the GPT3 model was used to provide pressure and temperature for PWV retrieval based on GNSS ZTD (GPT3-PWV model). The accuracy of the established models was validated using GNSS PWV retrieved from GNSS ZTD, ERA5 surface pressure, and temperature in the Guangxi region in 2022 as the reference value. The results show that, compared to the GPT3-PWV model, the estimation accuracy of the XGBZ-PWV, XGBZG-PWV, and XGBZE-PWV models improved by 22.98%, 29.03%, and 31.45%, respectively, with the XGBZE-PWV model performing the best. During two extreme rainfall events in 2022, the spatiotemporal evolution characteristics of PWV and rainfall were analyzed. The results demonstrate that the XGBZE-PWV model maintains good applicability even under extreme weather conditions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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