Estimation of soil moisture changes based on GRACE terrestrial water storage:Taking the monsoon region in eastern China as an example
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Soil moisture (SM) is significant in hydrological cycle with its frequent interaction between surface water and groundwater.Recently,there are two major methods of field measurement and remote sensingbased retrieval for monitoring SM.Although field SM measurement is widely used in practical applications,it fails to work in large scale areas with complex terrain for its inability to reflect spatial heterogeneity of SM.Moreover,the soil structure would be destructed due to its soil desiccation process.Remote sensingbased retrieval from optical or radar imagery is another major way to acquire SM at a large scale in space.The obstacle to the development and application of this technology is its limited maximum detection depth of 1-5 cm.SM levels affect a range of soil and plant dynamics.Surface SM is the water that is in the upper 10 cm of soil,whereas root zone SM is the water that is available to plantsgenerally considered to be in the upper 200 cm of soil.Consequently,one new method to obtain SM should be developed to overcome the problems above. Based on the principle of water balance,changes in terrestrial water storage (TWS) include three main parts:changes in surface water storage,SM,and groundwater storage.Detailly,changes in surface water storage are dominated by changes in snow depth and surface runoff.Changes in TWS were obtained from downscaled GRACE data,and changes in surface runoff and snow depth were from GLDAS NOAH data.Changes in groundwater from measured groundwater level were used to construct the water balance equation to calculate changes in SM.Then the SM change product was compared with other seven SM products of AMSR-E,ASCAT,ERA-Interim,MERRA2,GLDAS NOAH,GLDAS Mosaic,and GLDAS Catchment.All SM products were resampled to the same spatial resolution of 0.25°×0.25° for comparative analysis.Due to the inconsistent units of the various SM products,all data were normalized to the same range between 0 and 1.Furthermore,five statistical indicators were applied to validate the SM change product proposed.(1) Pearson correlation coefficient was used to verify the correlation of estimated SM change with other SM products in a time series.(2) Spatial auto-correlation coefficients (univariate Moran′s I) was used to verify how well the estimated SM change variability matches the spatial distribution of other SM change products.(3) The bivariate Moran′s I was used to reflect the correlation between the changes of estimated SM and other SM products in each grid point.(4) The root mean square error (RMSE) of the estimated SM change was compared to that of other SM products to assess the dispersion degree of all products.(5) The Mann-Kendall trend test was used to assess the trend of the time series of various SM change products and to verify the consistency of the trends for the estimated SM change with that of other products. Through the research,the following findings were made.Generally,the estimated SM changes showed a good temporal agreementwith each SM product in research area except the Yellow River basin and the Haihe River basin.This may be caused by the significant difference in the soil discrepancy characters during the permafrost and nonpermafrost periods in these basins,which affected the SM changes detected using microwaves such as AMSR-E and ASCAT.This discrepancy may amplified to some extent by the different methods of the discrepancy correction in different SM products.In terms of spatial distribution,a drying process was showed from the upper and middle reaches of the Yangtze River basin to the Huaihe River basin in spring;in summer,SM showed a south-wet,north-dry state;in autumn,the extent area of SM reduction gradually expanded southwards to the Pearl River basin;in winter,SM increased significantly in the upper reaches of the Yangtze River basin,and showed a slight increase in the lower reaches of the Yellow River basin and the Haihe River basin,while the rest of the research area were in the process of drying out.By analyzing the validation indicators,it is found that the estimated SM variation performed best agreementwith other products in the Pearl River basin,witch Pearson correlation coefficients are 0.70,0.79,0.86,0.85,0.81,0.50,and 0.51 with AMSR-E,ASCAT,ERA-Interim,MERRA2,GLDAS NOAH,GLDAS Mosaic,and GLDAS Catchment.The RMSE with other products is low,mostly controlled between 0.25 and 0.30.And the univariate Moran′s I was similar with [JP2]MERRA2,ASCAT,and GLDASCatchment (0.67,0.80,and 0.74),the bivariate Moran′s I was also[JP] similar with ERA-Interim,MERRA2,and ASCAT (0.70,0.70,and 0.77).The estimated SM changes showed poor consistency in the Yellow River basin,with Pearson correlation coefficients are all below 0.41,and the MK tests all show a near opposite trend to the other products,whitch may be the combined effect of retrieval errors in GRACE data due to the mountainous terrain in the western part of the Yellow River basin,the difficulty of land surface model data to accurately describe SM transport in loess soils,and the underrepresentation of measured groundwater data. The following main conclusions are made:(1) One method for estimating SM based on GRACE TWS was proposed.The SM obtained could keep more agreement with the connotation of SM in hydrology.It can overcome the limitations of field observations due to topography and number of stations,which may reflect spatial heterogeneity of SM of the whole study area.In addition,traditional remote sensing observation techniques can only detect the surface SM changes,while the proposed method can estimate SM of all soil layers above groundwater level.However,the estimation method does not consider the changes of biological water and the influence of human activities,which may introduce some uncertainty.(2) The estimated SM changes showed high spatial and temporal correlation with all other products in the Pearl River basin,which indicated that the estimation method is highly applicable in this basin.But in the Yellow River basin,the consistency of SM variation between the estimated SM changes and that of others products are not so good,which indicates that there are many other factors influencing SM variation in this basin and the uncertainty is high.Herewith,the research on SM estimation in the Yellow River basin needs to be strengthened in the future.
Keywords:
Project Supported:
Clc Number:
Mobile website
® 2024 All Rights Reserved
冀公网安备13010502002656号
冀公网安备13010502002656号