Applicability Evaluation of Six Gridded Soil Moisture Datasets in Inner Mongolia Autonomous Region

Song Haiqing; Zhu Zhongyuan; Li Yunpeng

doi:10.13961/j.cnki.stbctb.2021.06.025

您当前的位置：

首页 >

文章列表页 >

Applicability Evaluation of Six Gridded Soil Moisture Datasets in Inner Mongolia Autonomous Region

- Applicability Evaluation of Six Gridded Soil Moisture Datasets in Inner Mongolia Autonomous Region
- Bulletin of Soiland Water Conservation Vol. 41, Issue 6, Pages: 180-189(2021)
- 作者机构：
  
  1. 内蒙古农业大学水利与土木建筑工程学院,内蒙古,呼和浩特,010018
  2. 内蒙古自治区生态与农业气象中心,内蒙古,呼和浩特,010051
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2021.06.025
  CLC： S127;S152.7
- Published：2021
- 稿件说明：
移动端阅览
Song Haiqing, Zhu Zhongyuan, Li Yunpeng. Applicability Evaluation of Six Gridded Soil Moisture Datasets in Inner Mongolia Autonomous Region[J]. Bulletin of Soiland Water Conservation, 2021, 41(6): 180-189.
DOI：

Song Haiqing, Zhu Zhongyuan, Li Yunpeng. Applicability Evaluation of Six Gridded Soil Moisture Datasets in Inner Mongolia Autonomous Region[J]. Bulletin of Soiland Water Conservation, 2021, 41(6): 180-189. DOI： 10.13961/j.cnki.stbctb.2021.06.025.

摘要

[目的] 评价6套多源格点土壤水分数据的适用性，为内蒙古地区陆—气耦合研究、干旱监测和气候变化背景下陆地水资源监测等提供科学的土壤水分精度信息和选择依据。[方法] 以内蒙古地区2016—2020年5—9月63个气象台站逐旬0—10 cm土壤水分监测资料为基础，以相关系数、平均绝对误差和均方根误差为指标，系统评价了土壤水分主被动探测计划（SMAP）、欧洲航天局气候变化倡议（ESA）、中国气象局陆面数据同化系统（CLDAS）、欧洲中期天气预报中心第5代ERA5和ERA5Land以及美国全球陆面数据同化系统NOAH等6套格点土壤水分资料在内蒙古的精度和时空变化。[结果] ①6套格点土壤水分资料均可以较好地再现内蒙古土壤水分“东湿西干”的空间分布特征，SMAP在空间上与观测数据吻合最好，其余五套资料普遍高估了土壤水分。②从内蒙古及其3个气候分区土壤水分的时间序列统计特征来看，6套土壤水分格点数据较好地表现出了观测土壤水分的时间变化趋势，除了SMAP与实测最为接近外，其余五套数据在内蒙古自治区、区内半湿润区和半干旱区普遍偏湿，且ERA5和ERA5Land高估较多，在区内干旱区，SMAP，ERA5和ERA5Land与观测更为接近。③6套格点土壤水分资料在内蒙古及其3个分区均与观测值具有极显著的相关系数，SMAP，ESA，CLDAS，ERA5和ERA 5Land数据与实测数据的相关性明显优于NOAH，SMAP数据的平均绝对误差和均方根误差显著小于其他5套数据集。[结论] 格点6套土壤水分数据在内蒙古的适用性各异，SMAP数据质量总体上较其他五套资料最优，适用性最好，ESA数据其次，NOAH数据相对最差。

Abstract

[Objective] The applicability of six multi-source gridded soil moisture datasets was evaluated

in order to provide scientific precision information of soil moisture and selection basis for land-atmosphere coupling study

drought monitoring and land water resources monitoring under the background of climate change in Inner Mongolia Autonomous Region.[Methods] Using the soil moisture data observed by 63 meteorological stations in every ten days at 0-10 cm level from May to September from 2016 to 2020 in Inner Mongolia Autonomous Region

and taking correlation coefficient

mean absolute error and root mean square error as evaluation indexes

the accuracy and spatio-temporal variation of the SMAP soil moisture from NASA

ESA CCI from the European Space Agency(ESA)

CLDAS from the China Meteorological Administration(CMA)

ERA5 and ERA5 Land from the European Centre for Medium-Range Weather Forecasts(ECMWF) and NOAH from the global land surface data assimilation system(GLDAS/NASA) were systematically evaluated.[Results] ① Six gridded soil moisture datasets could truly reflected the spatial distribution characteristics of soil moisture in Inner Mongolia

and SMAP was the best. ② According to the statistical characteristics of the time series of soil moisture in Inner Mongolia Autonomous Region and its three climatic regions

the six gridded soil moisture datasets better reflected the temporal variation trend of soil moisture. SMAP was the closest to the measured data

and the other five datasets were generally higher in semi-humid and semi-arid regions of Inner Mongolia

and ERA5 and ERA5Land overestimated higher. In arid regions of Inner Mongolia

SMAP

ERA5 and ERA5Land were closer to the observations. ③ Six gridded soil moisture datasets in Inner Mongolia and its three sub-regions had very significant correlation coefficients

and the correlation coefficients between SMAP

ESA

CLDAS

ERA5

ERA5Land and the observed data was obviously better than that of NOAH. The mean absolute error and root mean square error of SMAP data are significantly smaller than those of the other five datasets.[Conclusion] Six gridded soil moisture datasets have different applicability in Inner Mongolia area. The quality and applicability of SMAP soil moisture are the best

ESA is the second

NOAH is the worst.

关键词

Keywords

references

张井勇, 吴凌云, 陆-气相互作用对东亚气候的影响[M].北京:气象出版社, 2014.

Chahine T M. The hydrological cycle and its influence on climate[J]. Nature, 1992, 359(6394):373-380.

马柱国, 符淙斌, 谢力, 等.土壤湿度和气候变化关系研究中的某些问题[J].地球科学进展, 2001, 16(4):563-566.

Robertson A, Frédéric V. Sub-seasonal to seasonal prediction:The gap between weather and climate forecasting[M]. Oxford:Elsevier, 2018.

Chen Yong, Yuan Huiling. Evaluation of nine sub-daily soil moisture model products over China using high-resolution in situ observations[J]. Journal of Hydrology, 2020, 588(9), 125054.

张文君, 周天军, 智海.土壤湿度影响中国夏季气候的数值试验[J].气象学报, 2012, 70(1):78-90.

赵娜娜, 于福亮, 李传哲, 等.降水入渗及土壤水分变化对产流过程影响研究进展[J].南水北调与水利科技, 2014, 12(2):111-115.

彭娜, 谢小立, 王开峰, 等.红壤坡地降雨入渗、产流及土壤水分分配规律研究[J].水土保持学报, 2006, 20(3):17-20.

Zhao Wei, Li Ainong. A review on land surface processes modelling over complex terrain[J]. Advances in Meteorology, 2015, 2015:1-17.

Koster R D, Dirmeyer P A, Guo Z, et al. Regions of strong coupling between soil moisture and precipitation[J]. Science, 2004, 305(5687):1138-1140.

Guo Zhichang, Dirmeyer P A, Hu Zengzhen, et al. Evaluation of the second global soil wetness project soil moisture simulations(2):Sensitivity to external meteorological forcing[J]. Journal of Geophysical Research, 2006, 111:1-11.

卢晨媛, 冯文兰, 王永前, 等.不同深度土壤水分同化产品在川西高原的应用[J].水土保持通报, 2021, 41(1):173-181.

李得勤, 张述文, 文小航, 等.土壤湿度参数化及对天气和气候模拟影响的研究进展[J].地球科学进展, 2016, 31(3):236-247.

Ford T W, Quiring S M. Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring[J]. Water Resources Research, 2019, 55(2):1565-1582.

Kumar S V, Wang S, Mocko D M, et al. Similarity assessment of land surface model outputs in the North American Land Data Assimilation System[J]. Water Resources Research, 2017, 53(11):8941-8965.

Xia Y, Sheffield J, Ek M B, et al. Evaluation of multi-model simulated soil moisture in NLDAS-2[J]. Journal of Hydrology, 2014, 512(5):107-125.

梁顺林, 李新, 谢先红.陆面观测、模拟与数据同化[M].北京:高等教育出版社, 2013.

陈泓羽, 吴静, 李纯斌, 等.卫星土壤水分产品在青藏高原地区的适用性评价[J].生态学报, 2020, 40(24):9195-9207.

高琪, 彭杰, 冯春晖, 等.基于Landsat 8数据的荒漠土壤水分遥感反演[J].水土保持通报, 2021, 41(1):125-132.

Bi Haiyun, Ma Jianwen, Zheng Wenjun, et al. Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau[J]. Journal of Geophysical Research Atmospheres, 2016, 121(6):2658-2678.

Tavakol A, Rahmani V, Quiring S M, et al. Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States[J]. Remote Sensing of Environment, 2019, 229:234-246.

顾润源.内蒙古自治区天气预报手册[M].北京:气象出版社, 2012.

Entekhabi D, Njoku E, O'Neill P, et al. The soil moisture active passive(SMAP) mission[J]. Proceedings of the IEEE, 2010, 98(5):704-716.

Wang Y, Quiring S M. Impact of soil moisture initializations on WRF-Simulated North American monsoon system[J]. Journal of Geophysical Research:Atmospheres, 2021, 126(4):1-23.

Reichle R H, Lannoy G D, Liu Q, et al. Global assessment of the SMAP Level-4 surface and root-zone soil moisture product using assimilation diagnostics[J]. Journal of Hydrometeorology, 2017, 18(12):3217-3237.

Dorigo W, Wagner W, Albergel C, et al. ESA CCI soil moisture for improved Earth system understanding:State-of-the art and future directions[J]. Remote Sensing of Environment, 2017, 203(15):185-215.

Ma Hongliang, Zeng Jiangyuan, Chen Nengcheng, et al. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI:A comprehensive assessment using global ground-based observations[J]. Remote Sensing of Environment, 2019, 231(12):1-44.

师春香, 姜立鹏, 朱智, 等.基于CLDAS 2.0驱动数据的中国区域土壤湿度模拟与评估[J].江苏农业科学, 2018, 46(4):231-236.

Cheng Meilin, Zhong Lei, Ma Yaoming, et al. A study on the assessment of multi-source satellite soil moisture products and reanalysis data for the Tibetan Plateau[J]. Remote Sensing, 2019, 11(10):1-18.

Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730):1999-2049.

Muñoz S J, Dutra, E, Agustí P, et al. ERA5-Land:A state-of-the-art global reanalysis dataset for land applications[J]. Earth System Science Data, 2021, https://doi.org/10.5194/essd-2021-82.

Rodell M, Houser P R, Jambor U, et al. The global land data assimilation system[J]. Bulletin of the American Meteorological Society, 2004, 85(3):381-394.

王文, 汪小菊, 王鹏. GLDAS月降水数据在中国区的适用性评估[J].水科学进展, 2014, 25(6):4-13.

He Jie, Yang Kun, Tang Wengjun, et al. The first high-resolution meteorological forcing dataset for land process studies over China[J]. Science Data, 2020, 7(1):1-11.

刘川, 余晔, 解晋, 等.多套土壤温湿度资料在青藏高原的适用性[J].高原气象, 2015, 34(3):653-665.

Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research (Atmospheres), 2001, 106(7):7183-7192.

宋海清, 李云鹏, 师春香, 等.内蒙古地区下垫面变化对土壤湿度数值模拟的影响[J].大气科学, 2016, 40(6):1165-1181.

Views

707

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Simulation of soil moisture dynamics under Hedysarum scoparium forests on fixed sand-dunes in eastern sandy land of Yellow River in Ningxia based on Hydrus-1D model

Regional characteristics of soil moisture and nutrients under artificial forests in typical loess regions

Influence of microtopography on spatiotemporal distribution of soil moisture during snowmelt period and short-term prediction

Evaluation of vegetation carrying capacity in loess regions along Xi’an-Yan’an high-speed railway

Effects of Paspalum wettsteinii plantation on surface soil water and heat dynamics in stabilized Benggang collapsing gully walls

Related Author

Zhang Weifu

Zhan Xiuli

Ma Siyi

Zhang Chengchun

Ma Xiaoxia

Ma Chi

Mo Baoru

Ma Quanlin

Related Institution

School of Geographic Science and Planning， Ningxia University

Afghanistan Joint Laboratory for International Cooperation on Characteristic Resources;and Environmental Governance in Arid Areas

Key Laboratory of;Resources Evaluation and Environmental Regulation in Arid Areas of Ningxia

Gansu Academy of Forestry

College of Life Sciences， Shihezi University

⁰