Analysis of spatial-temporal variability and heterogeneity of soil moisture.
CMLDS(2024)
摘要
An analysis of the spatial and temporal variations and distribution of soil moisture (SM) in Lanzhou City was performed to reveal the degree of influence that meteorological elements and the non-stationary relationship between time and space have on urban hydrological characteristics and agricultural production. This study is based on data from the reanalysis of daily soil moisture and meteorological observations from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the period 2000–2020. The geographic neural network weighted regression (GNNWR) model was used to explore the heterogeneous variation in soil moisture, relative humidity (RH), air temperature (TEMP), and precipitation (PRCP) across time and space, with correlation analyses used to investigate the relationship between SM and environmental factors. The results showed that: (1) the soil moisture, relative humidity, air TEMP, and PRCP in Lanzhou City vary seasonally, with their spatial distribution influenced by the Yellow River Basin; the closer to the river bank, the lower the RH and the higher the air TEMP. The RH and PRCP were observed to be higher on the south bank of the Yellow River. (2) The correlation between SM and environmental factors varies with time scale, with monthly RH, air TEMP, and PRCP showing a positive correlation with soil moisture. An obvious positive correlation was observed between SM and RH and a negative correlation between SM and air TEMP during the summer; however, no correlation was observed in the other seasons. (3) The environmental elements of SM within Lanzhou City and its surrounding region show heterogeneous variation in time and space where RH is positively influenced by the regional characteristics, with TEMP and PRCP varying according to location. The results of this study are useful for explaining the main drivers of SM and controlling the spatial and temporal variability of SM by managing the different environmental parameters within the region.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要