the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jan 2022
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
Abstract. Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.
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Interactive discussion
Status: closed
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RC1: 'Comment on essd-2022-21', Anonymous Referee #1, 14 Feb 2022
Review of “An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau” submitted to ESSD by Tian et al., 2021
The authors provided datasets of soil properties and long-term soil moisture over the Qilian Mountain based on in-situ observations. The dataset is very useful and important to help the scientific community to understand the soil hydrological processes, to improve the land surface modelling and to develop the soil moisture products over QTP. As the field sampling of profile soil samples and long-term maintenance of soil moisture stations over large scale mountainous areas is difficult, led to the scarce of the large scale in-situ SHP and SM dataset over QTP. Overall, this is a clearly written paper and the structure of the manuscript is well organized. The manuscript can be accepted after addressing my following questions.
Major comments:
- For the dataset, the spatial distribution of the soil properties datasets has been made public. Besides, it’s suggested to upload the representative original measurements of the soil properties (e.g. the key SHP datasets for the main land covers), which can be applied for large-scale modelling and ecohydrological study easily. I also noticed that the number of different soil properties varied at different layers. Please add the detail instruction of the specific number of each soil properties, which is important for its application.
- Table1: Why the observed profile SHP is calculated as the average of the surface SHP and subsurface SHP? In my opinion, it should be calculated using the depth-weighting method. The different calculation of profile SHP will influence the validation of profile soil datasets.
- From Figure 6 I can find that the spatial distribution of different soil properties is generated through different method, such as the ordinary Kriging method, the Cokriging method, and the Inverse Distance Weighted method. In my opinion, the different spatial pattern of soil properties can also be caused by the different methods. What’s more, what’s the spatial resolution of the soil properties dataset?
- Please notice that the spatial resolution of different SHP datasets or SM datasets are different, which will influence your validation. Please discuss it in the manuscript. Besides, the authors are suggested to validate the three SM products at daily scale instead of monthly scale.
Detailed comments:
- L11: Please change the “describing and predicting” to “describe and predict”
- L18: Please delete the “of” before “SM”.
- L35: Please change the “Earth” to “earth”.
- L41: Please change the “soil-sampling” to “soil sampling”
- L42-43: Please rewrite the sentence.
- L50: I think “and” should be replaced by “especially”.
- L51: Please use the abbreviation “SHPs” replace the “soil hydraulic properties”
- L52: I think that “individual” should be replaced by “different”.
- L60-61: The statement is unclear, please rewrite the sentence.
- L68-69: The statement is unclear, please rewrite the sentence.
- L72: Please change the “soil-property” to “soil property”.
- L83: Please change the “land-cover” to “land cover”.
- L95: Please check the name of the dataset, we can’t find the dataset in the website.
- L99: please delete the “mountainous”
- L106: It should be “study area”
- L109: Please change the “long-term” to “long term”
- L115: “Since the soil freezes in winter, SM data are only available for the growing seasons (May to October, Tian et al., 2019)”. Why only the SM data are only available for the growing seasons? I think the ECH2O 5TE probe can measure the liquid soil water content during winters.
- L122: Please delete the “a” before metal cylinder.
- L131: You have mentioned the size of the cylinder above, no need to mention it again.
- L145: Please delete “,” after “(also written as cm H2O)”
- L171: What is the specific depth of the surface layer and subsurface layer?
- L178: Please delete “types” after product.
- L201: Please unify the format of “CV” in the manuscript.
- L228: The parameter “l” should be n.
- Table3: What is log10Ks, please explain.
- L248: It should be Figure5, not Figure4.
- Figure7: The unit of bulk is wrong, please correct it.
- L314: Please change the “full profile SM” to “profile SM”
- L315: Please change the “or” to “and”
- L311: It’s Figure 10 not Figure 11.
- L333-334: Please check the value of spatial CV and temporal CV.
- Figure 9: Please check the legend of the soil moisture value.
- L353: What is the equation of NRMSE?
- L359-362: It’s better to move this sentence to the end of next paragraph.
- L361: What’s the performance of ZhangYG dataset?
- Figure 12: Please add the unit of the soil moisture
- L401: Please check the format here.
- L421: Please delete the “of after “understanding”.
- L428-L429: This paragraph is the discussion about soil moisture, please delete the “SHP”
- L442: Please change the “some” to “different”.
Citation: https://doi.org/10.5194/essd-2022-21-RC1 - AC1: 'Reply on RC1', Chansheng He, 22 Feb 2022
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RC2: 'Comment on essd-2022-21', Anonymous Referee #2, 04 Mar 2022
Overall, the paper is well written, and the data are valuable. However, I still have some main concerns:
- The authors give a good description of the SHP and SM datasets. However, they fail to clarify the accuracies of the ground-based datasets. Errors exist in either ground measurements or products. Direct comparisons between ground measurements and products can not help us understand the quality of the ground measurements. Although it is still challenging to quantify errors within the in-situ measurement, methods like triple collocation do exist that can give uncertainties of the in-situ measurements. I would strongly recommend the authors to try to explain the accuracy of the ground measurements, at least to cite some previous validation work to prove that the validation results of SHP and SM products in this work are consistent with them. This will make the quality of the ground measurements to be convincing.
- I believe that the long-time series point SM measurements are valuable and meaningful. However, I do not think they are suitable for validating coarse SM products. Since the in-situ measurements are all obtained from single stations, spatial heterogeneity impacts on the validation results can not be ignored. They should be considered, especially when evaluating SM products with a spatial resolution of tens kilometers using in-situ point measurements. Actually, dense in-situ SM observation networks are an effective way to minimize the impacts of spatial heterogeneity. Several dense in-situ SM observation networks within the Qinghai Tibet Plateau, such as Heihe network constructed during HiWATER, Naqu and Pali of the CTP-SMTMN networks and Maqu and Ngari of the Tibet-Obs networks, have provided long time-series SM measurements which can be well used for SM evaluation. Therefore, I would suggest the authors use point SM measurements in different applications on a small scale to clarify their quality.
Minor comments:
- L40, it is arbitrary to say “highly uncertain”.
- L105, in our stud area
- L130, make sure that it is “at the long-term SM monitoring stations” or “at the random sampling site”?
- Table 1, suggest to list spatial resolutions for HWSD, SoilGrid, ShangYG and DaiYJ.
- Table 2, suggest to list spatial resolutions for GLDAS, ERA5 and SMAP SM products.
- Line 230, double-check the numbers here. I can not well relate some of the numbers here to those listed in Table 3. For example, why n ranges within 0.09 and 0.12? why cv of clay ranges within (0.18, 0.28)? etc.
- L250, is it -0.66?
- L255, wrong space place in “… significant ,except…”
- Figure 5, keep the soil property name consistent with those in the text. E.g., bulk to bulk density or BD, s in logks should be a subscript
- L280, Theta_r and theta_s should be written formally.
- Figure 7, bulk, theta_s, Theta_r, and alpha should be written formally.
- L330, Figure 11 here should Figure 10? The “higher” in “…and this is higher than the temporal…” should be “lower”?
- Figure 9, the text 0.75 in the legend is wrong? Should it be 0.075? In addition, clarify in the text how to obtain figure 9? Same as figure 6 using Kriging method in ArcGIS to interpolate?
- L350, two meanings for PBIAS here. One is positive bias, the other is percent bias.
- Figure 11, use BD instead of bulk.
- L410, how can you conclude that “our SM dataset provides new accurate in-situ SM measurements covering …”?
Citation: https://doi.org/10.5194/essd-2022-21-RC2 - AC2: 'Reply on RC2', Chansheng He, 15 Mar 2022
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RC3: 'Comment on essd-2022-21', Anonymous Referee #3, 24 Apr 2022
Dear Editor,
Thank you so much for proving this oppertunity for me to review this paper. Please see my comments below.
Review of “An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau”
This paper provides potentially a very useful and important dataset. Substantial effort to collect soil samples and build up a long-term SM monitoring network in the high and cold mountainous region. Potentially a good candidate for ESSD. However, the important first-hand measured data cannot be accessible, for instance, SMST at the half-hourly scale on 32 LULC-Soil-DEM zones and measured SWRCs and possible soil heat conductivity curves, which hampers its potential to become a useful dataset in the hydrology, RS and soil research conducted on the high and cold mountainous region. The reviewer suggests the author uploading all raw data and completing the description data. Moreover, provide a brief description of the loaded data (in the data availability) that is consistent with the description in the manuscript. For detailed comments please see below. Some comments are labeled in the .pdf.
- In Line 69 about ‘a long-term SM dataset for the Qilian Mountains’, the reviewer knows the focus of this dataset is more about SM, while soil temperature information measured by ECH2O 5TE device should also be released for a comprehensive soil physical property information, which is more helpful in the use of soil water and heat transport (in LSM) research conducted on the high and cold mountainous region, as well as microwave signal simulation and the corresponding SM retrieval validation.
In line 114-115, it is mentioned that SM at different soil depths with a time interval of 30 min. The reviewer does notice this half-hourly data cannot be accessible. The reviewer suggests publishing SMST at the measured time scale rather than at the processed scale. Moreover, the reviewer does not think evaluating SM at the monthly scale is a routine, at least at the daily scale is more convincible.
- In the sheet ‘’station information’ of the uploaded file ‘soil moisture data_NE_QTP.xlsx’, there is no information of land use/type data, elevation, soil type and soil texture. In line 123, ‘Environmental factors such as the position, slope, aspect, root depth, and land cover were measured at each station’, please complete all these related information. In Figure 1, please also add the meaning of 32 main LULC (Land Use/Cover)-90 soil-DEM types, which is not clear for the reviewer who does not concern with LULC research.
- In the file of ‘SoilProfile_NE_QTP.nc’, there are 250 lat and 501 lon, please clarify how do these two correspond to the number of samples the author described in the paper.
- In line 140, please consider making the measured soil water retention curve (SWRC) data accessible. Peers are more interested in the raw data, which they can use to obtain parameters in other soil hydraulic models that they are interested.
- In Line 270, the author used Kriging method in ArcGIS to interpolate the spatial SHPs, please specify the Kriging method (e.g., what kind of method, any covariates and spatial resolution) and describe the uncertainty of this method and the interpolated data.
- Please explain Figure 7b.
- The reviewer thinks that the ‘dry bulk density’ is measured. Please refer to this soil property as dry bulk density in the manuscript and figures.
- Please make the symbols of and consistently used in the manuscript but also in the legend in Figures, e.g., In Figure 6, theta_s and theta_r.
- AC3: 'Reply on RC3', Chansheng He, 04 May 2022
-
RC4: 'Comment on essd-2022-21', Anonymous Referee #4, 04 May 2022
The authors presented a valuable work by observing soil properties in Heihe Basin. The data set is well organized and presented, while its strength is also demonstrated by comparing it to other data sets. I think it should be accepted by ESSD. I just have several minor comments about it, such as:
- the authors can compare the spatial distribution of the observed soil texture against HWSD and SoilGRiD, to show where these data differ significantly.
- From my understanding, GLDAS does not assimilate land surface information, and then its soil moisture is not improved too much. In contrast, SMAP-L4 is an assimilation product. So, if it is possible, the authors are suggested using some purely-remote sensing product instead of SMAP-L4.
Citation: https://doi.org/10.5194/essd-2022-21-RC4 - AC4: 'Reply on RC4', Chansheng He, 14 May 2022
Interactive discussion
Status: closed
-
RC1: 'Comment on essd-2022-21', Anonymous Referee #1, 14 Feb 2022
Review of “An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau” submitted to ESSD by Tian et al., 2021
The authors provided datasets of soil properties and long-term soil moisture over the Qilian Mountain based on in-situ observations. The dataset is very useful and important to help the scientific community to understand the soil hydrological processes, to improve the land surface modelling and to develop the soil moisture products over QTP. As the field sampling of profile soil samples and long-term maintenance of soil moisture stations over large scale mountainous areas is difficult, led to the scarce of the large scale in-situ SHP and SM dataset over QTP. Overall, this is a clearly written paper and the structure of the manuscript is well organized. The manuscript can be accepted after addressing my following questions.
Major comments:
- For the dataset, the spatial distribution of the soil properties datasets has been made public. Besides, it’s suggested to upload the representative original measurements of the soil properties (e.g. the key SHP datasets for the main land covers), which can be applied for large-scale modelling and ecohydrological study easily. I also noticed that the number of different soil properties varied at different layers. Please add the detail instruction of the specific number of each soil properties, which is important for its application.
- Table1: Why the observed profile SHP is calculated as the average of the surface SHP and subsurface SHP? In my opinion, it should be calculated using the depth-weighting method. The different calculation of profile SHP will influence the validation of profile soil datasets.
- From Figure 6 I can find that the spatial distribution of different soil properties is generated through different method, such as the ordinary Kriging method, the Cokriging method, and the Inverse Distance Weighted method. In my opinion, the different spatial pattern of soil properties can also be caused by the different methods. What’s more, what’s the spatial resolution of the soil properties dataset?
- Please notice that the spatial resolution of different SHP datasets or SM datasets are different, which will influence your validation. Please discuss it in the manuscript. Besides, the authors are suggested to validate the three SM products at daily scale instead of monthly scale.
Detailed comments:
- L11: Please change the “describing and predicting” to “describe and predict”
- L18: Please delete the “of” before “SM”.
- L35: Please change the “Earth” to “earth”.
- L41: Please change the “soil-sampling” to “soil sampling”
- L42-43: Please rewrite the sentence.
- L50: I think “and” should be replaced by “especially”.
- L51: Please use the abbreviation “SHPs” replace the “soil hydraulic properties”
- L52: I think that “individual” should be replaced by “different”.
- L60-61: The statement is unclear, please rewrite the sentence.
- L68-69: The statement is unclear, please rewrite the sentence.
- L72: Please change the “soil-property” to “soil property”.
- L83: Please change the “land-cover” to “land cover”.
- L95: Please check the name of the dataset, we can’t find the dataset in the website.
- L99: please delete the “mountainous”
- L106: It should be “study area”
- L109: Please change the “long-term” to “long term”
- L115: “Since the soil freezes in winter, SM data are only available for the growing seasons (May to October, Tian et al., 2019)”. Why only the SM data are only available for the growing seasons? I think the ECH2O 5TE probe can measure the liquid soil water content during winters.
- L122: Please delete the “a” before metal cylinder.
- L131: You have mentioned the size of the cylinder above, no need to mention it again.
- L145: Please delete “,” after “(also written as cm H2O)”
- L171: What is the specific depth of the surface layer and subsurface layer?
- L178: Please delete “types” after product.
- L201: Please unify the format of “CV” in the manuscript.
- L228: The parameter “l” should be n.
- Table3: What is log10Ks, please explain.
- L248: It should be Figure5, not Figure4.
- Figure7: The unit of bulk is wrong, please correct it.
- L314: Please change the “full profile SM” to “profile SM”
- L315: Please change the “or” to “and”
- L311: It’s Figure 10 not Figure 11.
- L333-334: Please check the value of spatial CV and temporal CV.
- Figure 9: Please check the legend of the soil moisture value.
- L353: What is the equation of NRMSE?
- L359-362: It’s better to move this sentence to the end of next paragraph.
- L361: What’s the performance of ZhangYG dataset?
- Figure 12: Please add the unit of the soil moisture
- L401: Please check the format here.
- L421: Please delete the “of after “understanding”.
- L428-L429: This paragraph is the discussion about soil moisture, please delete the “SHP”
- L442: Please change the “some” to “different”.
Citation: https://doi.org/10.5194/essd-2022-21-RC1 - AC1: 'Reply on RC1', Chansheng He, 22 Feb 2022
-
RC2: 'Comment on essd-2022-21', Anonymous Referee #2, 04 Mar 2022
Overall, the paper is well written, and the data are valuable. However, I still have some main concerns:
- The authors give a good description of the SHP and SM datasets. However, they fail to clarify the accuracies of the ground-based datasets. Errors exist in either ground measurements or products. Direct comparisons between ground measurements and products can not help us understand the quality of the ground measurements. Although it is still challenging to quantify errors within the in-situ measurement, methods like triple collocation do exist that can give uncertainties of the in-situ measurements. I would strongly recommend the authors to try to explain the accuracy of the ground measurements, at least to cite some previous validation work to prove that the validation results of SHP and SM products in this work are consistent with them. This will make the quality of the ground measurements to be convincing.
- I believe that the long-time series point SM measurements are valuable and meaningful. However, I do not think they are suitable for validating coarse SM products. Since the in-situ measurements are all obtained from single stations, spatial heterogeneity impacts on the validation results can not be ignored. They should be considered, especially when evaluating SM products with a spatial resolution of tens kilometers using in-situ point measurements. Actually, dense in-situ SM observation networks are an effective way to minimize the impacts of spatial heterogeneity. Several dense in-situ SM observation networks within the Qinghai Tibet Plateau, such as Heihe network constructed during HiWATER, Naqu and Pali of the CTP-SMTMN networks and Maqu and Ngari of the Tibet-Obs networks, have provided long time-series SM measurements which can be well used for SM evaluation. Therefore, I would suggest the authors use point SM measurements in different applications on a small scale to clarify their quality.
Minor comments:
- L40, it is arbitrary to say “highly uncertain”.
- L105, in our stud area
- L130, make sure that it is “at the long-term SM monitoring stations” or “at the random sampling site”?
- Table 1, suggest to list spatial resolutions for HWSD, SoilGrid, ShangYG and DaiYJ.
- Table 2, suggest to list spatial resolutions for GLDAS, ERA5 and SMAP SM products.
- Line 230, double-check the numbers here. I can not well relate some of the numbers here to those listed in Table 3. For example, why n ranges within 0.09 and 0.12? why cv of clay ranges within (0.18, 0.28)? etc.
- L250, is it -0.66?
- L255, wrong space place in “… significant ,except…”
- Figure 5, keep the soil property name consistent with those in the text. E.g., bulk to bulk density or BD, s in logks should be a subscript
- L280, Theta_r and theta_s should be written formally.
- Figure 7, bulk, theta_s, Theta_r, and alpha should be written formally.
- L330, Figure 11 here should Figure 10? The “higher” in “…and this is higher than the temporal…” should be “lower”?
- Figure 9, the text 0.75 in the legend is wrong? Should it be 0.075? In addition, clarify in the text how to obtain figure 9? Same as figure 6 using Kriging method in ArcGIS to interpolate?
- L350, two meanings for PBIAS here. One is positive bias, the other is percent bias.
- Figure 11, use BD instead of bulk.
- L410, how can you conclude that “our SM dataset provides new accurate in-situ SM measurements covering …”?
Citation: https://doi.org/10.5194/essd-2022-21-RC2 - AC2: 'Reply on RC2', Chansheng He, 15 Mar 2022
-
RC3: 'Comment on essd-2022-21', Anonymous Referee #3, 24 Apr 2022
Dear Editor,
Thank you so much for proving this oppertunity for me to review this paper. Please see my comments below.
Review of “An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau”
This paper provides potentially a very useful and important dataset. Substantial effort to collect soil samples and build up a long-term SM monitoring network in the high and cold mountainous region. Potentially a good candidate for ESSD. However, the important first-hand measured data cannot be accessible, for instance, SMST at the half-hourly scale on 32 LULC-Soil-DEM zones and measured SWRCs and possible soil heat conductivity curves, which hampers its potential to become a useful dataset in the hydrology, RS and soil research conducted on the high and cold mountainous region. The reviewer suggests the author uploading all raw data and completing the description data. Moreover, provide a brief description of the loaded data (in the data availability) that is consistent with the description in the manuscript. For detailed comments please see below. Some comments are labeled in the .pdf.
- In Line 69 about ‘a long-term SM dataset for the Qilian Mountains’, the reviewer knows the focus of this dataset is more about SM, while soil temperature information measured by ECH2O 5TE device should also be released for a comprehensive soil physical property information, which is more helpful in the use of soil water and heat transport (in LSM) research conducted on the high and cold mountainous region, as well as microwave signal simulation and the corresponding SM retrieval validation.
In line 114-115, it is mentioned that SM at different soil depths with a time interval of 30 min. The reviewer does notice this half-hourly data cannot be accessible. The reviewer suggests publishing SMST at the measured time scale rather than at the processed scale. Moreover, the reviewer does not think evaluating SM at the monthly scale is a routine, at least at the daily scale is more convincible.
- In the sheet ‘’station information’ of the uploaded file ‘soil moisture data_NE_QTP.xlsx’, there is no information of land use/type data, elevation, soil type and soil texture. In line 123, ‘Environmental factors such as the position, slope, aspect, root depth, and land cover were measured at each station’, please complete all these related information. In Figure 1, please also add the meaning of 32 main LULC (Land Use/Cover)-90 soil-DEM types, which is not clear for the reviewer who does not concern with LULC research.
- In the file of ‘SoilProfile_NE_QTP.nc’, there are 250 lat and 501 lon, please clarify how do these two correspond to the number of samples the author described in the paper.
- In line 140, please consider making the measured soil water retention curve (SWRC) data accessible. Peers are more interested in the raw data, which they can use to obtain parameters in other soil hydraulic models that they are interested.
- In Line 270, the author used Kriging method in ArcGIS to interpolate the spatial SHPs, please specify the Kriging method (e.g., what kind of method, any covariates and spatial resolution) and describe the uncertainty of this method and the interpolated data.
- Please explain Figure 7b.
- The reviewer thinks that the ‘dry bulk density’ is measured. Please refer to this soil property as dry bulk density in the manuscript and figures.
- Please make the symbols of and consistently used in the manuscript but also in the legend in Figures, e.g., In Figure 6, theta_s and theta_r.
- AC3: 'Reply on RC3', Chansheng He, 04 May 2022
-
RC4: 'Comment on essd-2022-21', Anonymous Referee #4, 04 May 2022
The authors presented a valuable work by observing soil properties in Heihe Basin. The data set is well organized and presented, while its strength is also demonstrated by comparing it to other data sets. I think it should be accepted by ESSD. I just have several minor comments about it, such as:
- the authors can compare the spatial distribution of the observed soil texture against HWSD and SoilGRiD, to show where these data differ significantly.
- From my understanding, GLDAS does not assimilate land surface information, and then its soil moisture is not improved too much. In contrast, SMAP-L4 is an assimilation product. So, if it is possible, the authors are suggested using some purely-remote sensing product instead of SMAP-L4.
Citation: https://doi.org/10.5194/essd-2022-21-RC4 - AC4: 'Reply on RC4', Chansheng He, 14 May 2022
Data sets
An in situ observation datset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau Chansheng, He; Jie, Tian; Xuejing, Wang; Lanhui, Zhang; Baoqing, Zhang; Yibo, Wang https://doi.org/10.5281/zenodo.5830583
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Cited
Jie Tian
Baoqing Zhang
Xuejin Wang
Chansheng He
This preprint has been withdrawn.
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