基于转移熵的长江流域土壤湿度对降水反馈研究
Soil Moisture Feedbacks on Precipitation in the Yangtze River Catchment Based on Transfer Entropy
DOI: 10.12677/JWRR.2021.101003, PDF,    国家自然科学基金支持
作者: 娄 巍, 刘 攀*, 程 磊, 邹铠杰, 夏 倩：武汉大学水资源与水电工程科学国家重点实验室，湖北 武汉
关键词: 土壤湿度对降水反馈；长江流域；转移熵；Soil Moisture-Precipitation Feedback； The Yangtze River； Transfer Entropy
摘要: 陆气耦合过程复杂，研究土壤湿度对降水的影响机制，对气候预测和天气预报具有重要意义。目前土壤湿度对降水的反馈机制尚不明确，采用物理模型存在较大的不确定性，而通过数据直接反映非线性统计相关性的转移熵，是一种可解释水文系统的新范式，为研究此类问题提供了可能。本文提出并验证了归一化转移熵，使量纲不同的耦合强度具备可比性；使用长江流域2002~2018年168个气象站点观测的降水和蒸散发数据、全球陆面参数数据LPDR V2.0中的土壤湿度数据、以及MODIS中的植被指数NDVI数据，采用显著滞时、不同滞时的相对预测度和归一化信息熵3个信息指标识别土壤湿度对降水的反馈特征，并应用偏相关方法验证了长江流域土壤湿度对降水正反馈的合理性。结果表明：1) 土壤湿度对降水的平均显著滞时为4.3 d，与降水对土壤湿度的1.8 d相比滞时更长，说明土壤湿度对降水的反馈过程存在滞时，即土壤湿度响应前期降水后，延迟较长时段影响降水过程；2) 降水对土壤湿度的归一化转移熵为0.51，与土壤湿度对降水的0.13相比耦合强度更高，说明土壤湿度对降水的反馈显著弱于降水对土壤湿度的影响；3) 金沙江下游和洞庭湖、鄱阳湖、太湖水系等近湖区域由于水量交换频繁，土壤湿度对降水反馈较快且强度更高。
Abstract: The land surface and atmosphere interact as a complexly linked system. The soil moisture-precipitation feedback mechanism is of great significance to weather forecast and climate prediction; however, it is uncertain in physical-based numerical simulation. Transfer entropy, which reflects non-linear statistical correlation directly through data, has been considered as a new paradigm to explain hydrological system and provides possibility to study the land-atmosphere coupling. Normalized transfer entropy was proposed in this study, and was verified to measure comparable coupling strength. Observation data on 168 weather stations during 2002~2018, MODIS-NDVI (MOD13A2) and soil moisture data from the global land parameters data record LPDR V2.0 were used. Characteristics of the feedback were explored by three information indexes. A positive soil moisture-precipitation feedback was also valid by the partial correlation. The results showed: 1) the average significant lag time of soil moisture-precipitation coupling was 4.3 d, which was longer than 1.8 d of precipitation-soil moisture coupling. It indicated that there was a delay effect during the feedback process. The soil moisture in response to precipitation affected the precipitation process after a longer period of time; 2) the normalized transfer entropy of precipitation-soil moisture coupling was 0.51, which was stronger than 0.13 of soil moisture -precipitation coupling; and 3) due to the frequent water exchange in the lower reaches of the Jinsha River, the Dongting Lake, the Poyang Lake and the Taihu Lake systems, soil moisture had a faster and stronger feedback on precipitation.
文章引用：娄巍, 刘攀, 程磊, 邹铠杰, 夏倩. 基于转移熵的长江流域土壤湿度对降水反馈研究[J]. 水资源研究, 2021, 10(1): 21-32. https://doi.org/10.12677/JWRR.2021.101003

参考文献

[1] 李泽君. 陆-气反馈关系的统计特征识别及建模研究[D]: [博士学位论文]. 武汉: 武汉大学, 2019. LI Zejun. Statistical relationships identifying and modeling for land-atmosphere feedback. Ph.D. Thesis, Wuhan: Wuhan University, 2019. (in Chinese)
[2] 刘源. 中国境内土壤湿度-降水耦合强度研究[D]: [硕士学位论文]. 兰州: 兰州大学, 2016. LIU Yuan. A study of soil mositure-precipitation coupling strength in China. Master’s Thesis, Lanzhou: Lanzhou University, 2016. (in Chinese)
[3] 王健. 土壤湿度变化对全球陆-气耦合热点地区近地层温度影响的研究[D]: [硕士学位论文]. 兰州: 兰州大学, 2018. WANG Jian. Impact of soil moisture variations on near-surface temperature of land-atmosphere coupling hot spot regions. Master’s Thesis, Lanzhou: Lanzhou University, 2018. (in Chinese)
[4] FINDELL, K. L., ELTAHIR, E. A. B. An analysis of the soil moisture-rainfall feedback, based on direct observations from Illinois. Water Resources Research, 1997, 33(4): 725-735. [Google Scholar] [CrossRef] 
[5] SALVUCCI, G. D., SALEEM, J. A., and KAUFMANN, R. Investigating soil moisture feedbacks on precipitation with tests of Granger causality. Advances in Water Resources, 2003, 25(8): 1305-1312. [Google Scholar] [CrossRef] 
[6] FREMME, A., SODEMANN, H. The role of land and ocean evaporation on the variability of precipitation in the Yangtze River valley. Hydrology and Earth System Sciences, 2019, 23(6): 2525-2540. [Google Scholar] [CrossRef] 
[7] KOSTER, R. D., DIRMEYER, P. A., GUO, Z. C., et al. Regions of strong coupling between soil moisture and precipitation. Science, 2004, 305(5687): 1138-1140. [Google Scholar] [CrossRef] [PubMed]
[8] CHOW, K. C. Time-lagged effects of spring Tibetan Plateau soil moisture on the monsoon over China in early summer. International Journal of Climatology, 2007, 28(1): 55-67. [Google Scholar] [CrossRef] 
[9] 王林, 王磊, 等. 青藏高原春季土壤湿度对长江中下游地区初夏短期气候影响的数值模拟[J]. 成都信息工程大学学报, 2017(2): 81-88. WANG Lin, WANG Lei, et al. An numerical simulation on the effect of spring soil moisture in Tibetan Plateau on early summer short-term climate over middle and lower reaches of Yangtze River. Journal of Chengdu University of Information Technology, 2017(2): 81-88. (in Chinese)
[10] Boé, J. Modulation of soil moisture-precipitation interactions over France by large scale circulation. Climate Dynamics, 2012, 40(3-4): 875-892. [Google Scholar] [CrossRef] 
[11] KUMAR, P., GUPTA, H. V. Debates—Does information theory provide a new paradigm for earth science? Water Resources Research, 2020, 56(2): 1-13. [Google Scholar] [CrossRef] 
[12] BRUNSELL, N. A. A multiscale information theory approach to assess spatial-temporal variability of daily precipitation. Journal of Hydrology, 2010, 385(1): 165-72(8). [Google Scholar] [CrossRef] 
[13] GOODWELL, A. E., KUMAR, P. Temporal information partitioning networks (TIPNets): A process network approach to infer ecohydrologic shifts. Water Resources Research, 2017, 53(7): 5899-5919. [Google Scholar] [CrossRef] 
[14] GOODWELL, A. E., KUMAR, P. Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables. Water Resources Research, 2017, 53(7): 5920-5942. [Google Scholar] [CrossRef] 
[15] SILVA, V. D. P. R. D., FILHO, A. F. B., ALMEIDA, R. S. R., et al. Shannon information entropy for assessing space-time variability of rainfall and streamflow in semiarid region. Science of the Total Environment, 2016, 544: 330-338.[CrossRef] [PubMed]
[16] FRANZEN, S. E., FARAHANI, M. A., and GOODWELL, A. E. Information flows: Characterizing precipitation-stream flow dependencies in the Colorado Headwaters with an information theory approach. Water Resources Research, 2020, 56(10).[CrossRef] 
[17] SCHREIBER, T. Measuring information transfer. Physical Review Letters, 2000, 85(2): 461-464. [Google Scholar] [CrossRef] 
[18] ANDREW, B., BART, N., GENGXIN, O., et al. Quantifying process connectivity with transfer entropy in hydrologic models. Water Resources Research, 2019, 55(6): 4613-4629. [Google Scholar] [CrossRef] 
[19] GOODWELL, A. E., KUMAR, P. A changing climatology of precipitation persistence across the United States using information-based measures. Journal of Hydrometeorology, 2019, 20(8): 1649-1666. [Google Scholar] [CrossRef] 
[20] DU, J. Y., KIMBALL, et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave earth observations. Earth System Science Data, 2017, 9(2): 791-808. [Google Scholar] [CrossRef] 
[21] 苏辉. 降水与土壤湿度因果关系分析及预测研究[D]: [硕士学位论文]. 杭州: 杭州电子科技大学, 2018. SU Hui. Causality analysis between precipitation and soil moisture and precipitation prediction. Master’s Thesis, Hangzhou: Hangzhou Dianzi University, 2018. (in Chinese)
[22] LAM, A., BIERKENS, M. F. P., and VAN DEN HURK, B. J. J. M. Global patterns of relations between soil moisture and rainfall occurrence in ERA-40. Journal of Geophysical Research: Atmospheres, 2007, 112(D17): D17116. [Google Scholar] [CrossRef] 
[23] MEI, R., WANG, G. Summer land-atmosphere coupling strength in the United States: Comparison among observations, reanalysis data, and numerical models. Journal of Hydrometeorology, 2012, 13(3): 1010-1022. [Google Scholar] [CrossRef] 
[24] 邹海波. 鄱阳湖湖效应降水的统计分析与个例研究[D]: [博士学位论文]. 兰州: 兰州大学, 2020. ZOU Haibo. Statistical analysis and cases studies of lake-effect precipitation over Poyang Lake. Ph.D. Thesis, Lanzhou: Lanzhou University, 2020. (in Chinese)