| 摘要: |
| 陆地水储量是赋存在陆地上各种形式水的综合体现，研究其时空变化对认识区域水循环过程和水资源调控等具有重要意义。然而现有陆地水储量变化数据实际分辨率较低，限制了其在中小流域或地区中的应用。针对这一问题，本文基于GRACE重力卫星和其后续卫星GRACE-FO反演的陆地水储量变化数据，首先采用随机森林模型，分别基于格点、区域（流域）和区域（全国）3种空间降尺度思路将GRACE数据降尺度至0.25°×0.25°，后结合GLDAS模型数据，基于水量平衡原理计算得到地下水储量变化数据，最后基于降尺度模型模拟效果和实测地下水位数据评估3种降尺度思路在全国的适用性。结果表明：随机森林模型能够较好地模拟驱动数据（降水、气温、植被条件指数和土壤水储量）与GRACE数据的统计关系，验证期格点降尺度思路的平均相关系数总体在0.6左右，区域降尺度思路的平均纳什效率系数、相关系数和均方根误差分别>0.5、>0.75和<6.6 cm，3种空间降尺度思路的模拟精度均满足基本要求；2003—2021年间，GRACE数据、格点降尺度、区域降尺度（流域）和区域降尺度（全国）得到的我国陆地水储量亏缺量分别约为119.5×108、62.4×108、121.1×108和121.8×108 m3/a，地下水储量亏缺量分别约为230.0×108、171.8×108、235.6×108和236.4×108 m3/a，受制于样本数较少等原因，格点降尺度结果精度较差；两种区域降尺度思路得到的水储量变化速率均和原始GRACE数据基本一致，模拟结果均优于格点降尺度，且细化到流域的区域降尺度对地下水储量变化验证精度有一定的改善。区域降尺度具有适用性强、模拟精度高、计算效率高的优势，研究结果可为流域水资源可持续利用以及水资源规划等提供精细化的水储量变化数据。 |
| 关键词: 水储量变化 GRACE 随机森林模型 统计降尺度 中国 |
| DOI： |
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| 基金项目:国家重点研发计划项目，水利部重大科技项目，国家自然科学基金项目 |
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| Comparison of three spatial downscaling concepts of GRACE data using random forest model |
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CHU Jiangdong1, SU Xiaoling1, ZHANG Te1, LEI Yisu1, JIANG Tianliang2, WU Haijiang1, WANG Qianyu3
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1.Key Laboratory for Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University;2.China Institute of Water Resources and Hydropower Research;3.Key Laboratory for Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education,Northwest A F University
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| Abstract: |
| Terrestrial water storage is a comprehensive manifestation of land water. Analyzing the spatio-temporal changes of terrestrial water storage is vital for improving the understanding of hydrological processes and water resource management. However, the low spatial resolution of existing terrestrial water storage anomalies derived from GRACE limits their applications in small and medium basins. To improve their spatial resolution, the random forest models were utilized to downscale terrestrial water storage anomalies data derived from GRACE satellites and its follow-up mission GRACE-Follow On into 0.25°×0.25° spatial resolution at three spatial scales, including grid cell, regional (basin) and regional (China). Groundwater storage anomalies were calculated by combining the vertical water budget and GLDAS model output. The performance of the downscaling data was evaluated based on models’ indicators and in-situ groundwater levels across China. Results show that random forest model can accurately establish the statistical relationship between input variables (precipitation, temperature, vegetation condition index, and soil water storage) and GRACE data. The average correlation coefficient of the grid cell downscaling method during the validation period is generally around 0.6. And, the average Nash efficiency coefficient, correlation coefficient and root mean square error of the regional downscaling method are greater than 0.5, 0.75 and less than 6.6 cm, respectively. Overall, the accuracy of different downscaled data was promising. From 2003 to 2021, The deficit of China’s terrestrial water storage from original, grid cell downscaling-based, regional downscaling-based (basin) and regional downscaling-based (China) GRACE data were about 119.5×108m3/yr, 62.4×108 m3/yr, 121.1×108 m3/yr and 121.8×108 m3/yr, respectively. The storage of groundwater storage was approximately 230.0×108 m3/yr, 171.8×108 m3/yr, 235.6×108 m3/yr and 236.4×108 m3/yr, respectively. The simulation accuracy of the grid cell downscaling results is relatively poor due to the small sample size. Change rates of water storage obtained by the regional downscaling methods were generally consistent with the original GRACE data, indicating that the regional downscaling methods were better than the grid-cell downscaling method. Compared with the grid-cell downscaling method, results obtained by the regional downscaling method were smoother in space, and the regional downscaling (basin) refined to the basin could improve the accuracy of groundwater storage anomalies. Regional downscaling has the advantages of strong applicability, high simulation accuracy and high computational efficiency than grid cell downscaling. Findings of this study can provide refined water storage anomalies data for sustainable utilization and water resources planning at basin-scale. |
| Key words: water storage anomalies GRACE random forest model statistical downscaling China |
