1. 土壤水分数据融合及其在旱涝灾害多维度评估中的应用.
- Author
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张 蕾, 郭安红, 宋迎波, 何 亮, 赵晓凤, and 赵运成
- Subjects
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STANDARD deviations , *SOIL moisture , *SUPPORT vector machines , *TYPHOONS , *CLAY soils , *RANDOM forest algorithms - Abstract
Soil moisture is one of the most important components in the land-air coupling system. Acquisition of soil relative moisture in high precision can benefit to the finer monitoring and assessment, as well as prediction and warning for drought and waterlogging. This study aims to assess drought and waterlogging using data fusion of soil water in multiple dimensions. Daily soil relative moisture was derived from China Meteorological Administration Land surface data assimilation system (CLDAS). Hourly soil relative moisture was observed from automatic soil moisture stations during April and November from the year of 2020 to 2023. The dataset also included the land use, soil properties and geographic information. A dynamic fusion model was constructed to correct the bias of relative soil moisture from between automatic soil moisture stations and CLDAS. Random forest and support vector machine models were used to take the latitude, longitude, altitude, soil sand, soil silt and soil clay as the inputs. Given that daily fusion relative soil moisture was constructed, the intensity, area and duration index were proposed to monitor and assess drought and waterlogging disasters at the multiple dimensions. The results showed that the values of determination coefficient between observed and corrected soil relative moisture by random forest model were 0.79, 0.81 and 0.80, respectively, and relative root mean square errors were 13.81%, 11.40% and 9.50%, respectively, at the length of 0-10, 0- 20 and 0-50 cm. Comparatively, the determination coefficient was 0.56-0.57 and relative root mean square error was 17.72%- 23.63% between observed and corrected relative soil moisture by support vector machine model. Thus, random forest model was accepted to effectively correct the bias of relative soil moisture between automatic soil moisture stations and CLDAS. Daily fused relative soil moisture was then generated as well. At the spatial scale, the days percent of water deficit increased from southeast to northwest in the period of April-November. Moreover, the days percent of water deficit in the central-western areas of Inner Mongolia and majority of Northwest China was greater than 70% and even 80%, while 50%-70% was found in northeastern Inner Mongolia, central-northern North China and central-western Southwest China, even less than 40% in major central-eastern areas. The days percent of soil overwetting decreased from southeast to northwest, with the value of greater than 50% only in eastern and southern South China, southern Southeast China and northeastern Northeast China. According to the water deficit index, overwetting, moisture, the area and duration index, drought and waterlogging conditions were dynamic assessed in the quantitative and finer characteristics at the intensity-area-duration dimension, especially in the typical disaster process, such as typical heat and drought event in Yangtze River in the year of 2022, typhoon Dusuri and Khanun in the year of 2023. Typically, the soil water deficit was tended to intensify in Yangtze River in the summer of 2022. The overwetting index was much lower during April-July in Huang-Huai-Hai region, whereas, there was the significant increase in late July by typhoon Dusuri, which was characterized by 12.3%-70.1% area exposed to waterlogging in 3-5 days. In Northeast China, the area exposed to overwetting or waterlogging in a large extent during 2-4th August in Heilongjiang and Jilin by typhoon Dusuri. After that, there was some decrease in 5-9th August, but it enlarged to 27.4%-41.8% in 10-13th August by typhoon Khanun. Of course, further investigation should be explored to consider more inducing factors on soil moisture. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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