Relationship between soil moisture and climate and its memory in Southwest China

Yuanhong, 邓元红 Deng; Shijie, 王世杰 Wang; Xiaoyong, 白晓永 Bai; Luhua, 吴路华 Wu; Yue, 操玥 Cao; Chaojun, 李朝君 Li; Huiwen, 李汇文 Li; Zeyin, 胡泽银 Hu

doi:10.5846/stxb201807301613

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Precipitation is an important source of SM supply (Deng et al, 2018). Thus, under conditions that stations have observed precipitation data, this paper chose stations with the strongest positive and negative correlations for the six SM products from 670 stations, respectively.…”

Section: Preliminary Study Of Negative Correlation Based On Gauged mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Deng

Wang

Bai

et al. 2019

Hydrological Processes

View full text Add to dashboard Cite

High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C-and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December-February and June-August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differobviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products. K E Y W O R D S

show abstract

Section: Preliminary Study Of Negative Correlation Based On Gauged mentioning

confidence: 99%

“…Soil moisture (SM) is an important terrestrial water cycle component that not only influences evaporation, infiltration, and runoff formation (Sorman & Abdulrazzak, 1995;Tayfur, Zucco, Brocca, & Moramarco, 2014) but also affects climate system by redistributing sensible and latent heat (Deng et al, 2018;LeMone et al, 2007).…”

Section: Introductionmentioning

confidence: 99%