Evaluation of root-zone soil moisture over the Huai river basin
Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. RZSM is not directly observable from space, but several RZSM products are available and widely used at global and continental scales. This paper presents a comprehensive quantitative evaluation of eight RZSM products over the Huai River Basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors that increase the uncertainties of model-based RZSM, such as errors in atmospheric forcing (precipitation, air temperature), soil properties, and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and the lowest unbiased root mean square error (ubRMSE): 0.69 and 0.018 m3 m−3, respectively. All RZSM products tend to overestimate in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimation of Surface Soil Moisture (SSM) in SMOS L3, caused by underestimated physical surface temperature and overestimated ERA interim soil moisture, triggers the underestimation of RZSM in SMOS L4. The overestimation of RZSM by the other products can be explained by the overestimation of precipitation, the frequency of precipitation events (drizzle effects) and the underestimation of air temperature. In addition, the overestimation of soil clay content and the underestimation of soil sand content in different LSMs lead to higher soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM have the highest correlation, which can be attributed to the fact that both products use the catchment land surface model and the atmospheric forcing provided by the Goddard Earth Observing System Model, version 5 (GEOS-5), although the versions differ slightly.
En Liu et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2023-1597', Anonymous Referee #1, 07 Nov 2023
RC2: 'Comment on egusphere-2023-1597', Anonymous Referee #2, 10 Nov 2023
- AC3: 'Reply on RC2', Yonghua Zhu, 02 Dec 2023
RC3: 'Comment on egusphere-2023-1597', Anonymous Referee #3, 23 Nov 2023
- AC4: 'Reply on RC3', Yonghua Zhu, 02 Dec 2023
En Liu et al.
In situ root-zone soil moisture measurements https://doi.org/10.6084/m9.figshare.23497502
ERA5 reanalysis datasets Hourly 0.25 x 0.25 degree| ECMWF https://doi.org/10.24381/cds.adbb2d47
GES DISC Dataset: MERRA-2 tavg1_2d_lnd_Nx (M2T1NXLND 5.12.4) (nasa.gov) https://doi.org/10.5067/VJAFPLI1CSIV
CISL RDA: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products (ucar.edu) https://doi.org/10.5065/D61C1TXF
GES DISC Dataset: GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1 (nasa.gov) https://doi.org/10.5067/E7TYRXPJKWOQ
GES DISC Dataset: GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2 (nasa.gov) https://doi.org/10.5067/TXBMLX370XX8
China Meteorological Administration Land Data Assimilation System (CLDAS v2.0) Product Dataset (cma.cn) http://data.cma.cn/en/?r=search/uSearch&keywords=cldas
SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 5 | National Snow and Ice Data Center (nsidc.org) https://doi.org/10.5067/9LNYIYOBNBR5
L4 Land research products-Centre Aval de Traitement des Données SMOS (CATDS) http://dx.doi.org/10.12770/316e77af-cb72-4312-96a3-3011cc5068d4
En Liu et al.
Viewed (geographical distribution)