As an essential property of frozen soils,change of unfrozen water content(UWC)with temperature,namely soil-freezing characteristic curve(SFCC),plays significant roles in numerous physical,hydraulic and mechanical proc...As an essential property of frozen soils,change of unfrozen water content(UWC)with temperature,namely soil-freezing characteristic curve(SFCC),plays significant roles in numerous physical,hydraulic and mechanical processes in cold regions,including the heat and water transfer within soils and at the land–atmosphere interface,frost heave and thaw settlement,as well as the simulation of coupled thermo-hydro-mechanical interactions.Although various models have been proposed to estimate SFCC,their applicability remains limited due to their derivation from specific soil types,soil treatments,and test devices.Accordingly,this study proposes a novel data-driven model to predict the SFCC using an extreme Gradient Boosting(XGBoost)model.A systematic database for SFCC of frozen soils compiled from extensive experimental investigations via various testing methods was utilized to train the XGBoost model.The predicted soil freezing characteristic curves(SFCC,UWC as a function of temperature)from the well-trained XGBoost model were compared with original experimental data and three conventional models.The results demonstrate the superior performance of the proposed XGBoost model over the traditional models in predicting SFCC.This study provides valuable insights for future investigations regarding the SFCC of frozen soils.展开更多
There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model （LSM） to improve the description of hydrological processes in them. For incorporating with the Noah LS...There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model （LSM） to improve the description of hydrological processes in them. For incorporating with the Noah LSM, a new rainfall-runoff model named XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL） was developed and presented in this study, based on the soil moisture storage capacity distribution curve （SMSCC）, some essential modules of the Xinanjiang model, together with the simple model framework of the TOPMODEL （a topography based hydrological model）. The innovation of XXT is that the water table is incorporated into SMSCC and it connects the surface runoff production with base flow production. This improves the description of the dynamically varying saturated areas that produce runoff and also captures the physical underground water level. XXT was tested in a small-scale watershed Youshuijie （946 km2） and a large-scale watershed Yinglouxia （10009 km2） in China. The results show that XXT has better performance against the TOPMODEL and the Xinanjiang model for the two watersheds in both the calibration period and the validation period in terms of the Nash-Sutcliffe efficiency. Moreover, XXT captures the largest peak flow well for both the small： and large-scale watersheds during the validation period, while the TOPMODEL produces significant overestimates or underestimates, so does the Xinanjiang model.展开更多
The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validati...The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares(PLS)analysis.Spectral data were collected using a spectrophotometer.Fiber height of 100 mm and light source angle at 45°were chosen to obtain the sharpest spectra without apparent scattering effect.The results show that moisture content and particle size strongly influenced the absorbance of the spectra,and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5-1.0,1.0-2.0 and 2.0-5.0 mm,with the correlation coefficients(r)of 0.819,0.815 and 0.818,and standard errors of prediction(SEP)of 2.29,2.41 and 2.42 mg/kg,respectively.Poor N prediction model was obtained when the soil was kept in its natural moisture content with r of 0.575 and SEP of 3.275 mg/kg,compared to the performance of dried soil samples with r of 0.815 and SEP of 2.425 mg/kg.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42177291)Innovation Capability Support Program of Shaanxi Province(2023-JC-JQ-25 and 2021KJXX-11).
文摘As an essential property of frozen soils,change of unfrozen water content(UWC)with temperature,namely soil-freezing characteristic curve(SFCC),plays significant roles in numerous physical,hydraulic and mechanical processes in cold regions,including the heat and water transfer within soils and at the land–atmosphere interface,frost heave and thaw settlement,as well as the simulation of coupled thermo-hydro-mechanical interactions.Although various models have been proposed to estimate SFCC,their applicability remains limited due to their derivation from specific soil types,soil treatments,and test devices.Accordingly,this study proposes a novel data-driven model to predict the SFCC using an extreme Gradient Boosting(XGBoost)model.A systematic database for SFCC of frozen soils compiled from extensive experimental investigations via various testing methods was utilized to train the XGBoost model.The predicted soil freezing characteristic curves(SFCC,UWC as a function of temperature)from the well-trained XGBoost model were compared with original experimental data and three conventional models.The results demonstrate the superior performance of the proposed XGBoost model over the traditional models in predicting SFCC.This study provides valuable insights for future investigations regarding the SFCC of frozen soils.
基金Supported by the National Basic Research and Development (973) Program of China (2010CB951404)National Nature Science Foundation of China (40971024 and 31101073)+2 种基金Natural Science Research Fund of the Education Department of Sichuan Province (09ZA075)Open Research Fund of the Meteorological Center for Huaihe Watershed (HRM200905)China Meteorological Administration Special Public Welfare Research Fund (GYHY200906007)
文摘There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model （LSM） to improve the description of hydrological processes in them. For incorporating with the Noah LSM, a new rainfall-runoff model named XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL） was developed and presented in this study, based on the soil moisture storage capacity distribution curve （SMSCC）, some essential modules of the Xinanjiang model, together with the simple model framework of the TOPMODEL （a topography based hydrological model）. The innovation of XXT is that the water table is incorporated into SMSCC and it connects the surface runoff production with base flow production. This improves the description of the dynamically varying saturated areas that produce runoff and also captures the physical underground water level. XXT was tested in a small-scale watershed Youshuijie （946 km2） and a large-scale watershed Yinglouxia （10009 km2） in China. The results show that XXT has better performance against the TOPMODEL and the Xinanjiang model for the two watersheds in both the calibration period and the validation period in terms of the Nash-Sutcliffe efficiency. Moreover, XXT captures the largest peak flow well for both the small： and large-scale watersheds during the validation period, while the TOPMODEL produces significant overestimates or underestimates, so does the Xinanjiang model.
基金This study was supported by National Science and Technology Support Program(2006BAD10A09)863 National High-Tech Research and Development Plan(2007AA10Z210)+1 种基金the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,P.R.China.and Natural Science Foundation of China(Project No:30671213)Natural Science Foundation of Zhejiang Province(Project No:Y307119).
文摘The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares(PLS)analysis.Spectral data were collected using a spectrophotometer.Fiber height of 100 mm and light source angle at 45°were chosen to obtain the sharpest spectra without apparent scattering effect.The results show that moisture content and particle size strongly influenced the absorbance of the spectra,and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5-1.0,1.0-2.0 and 2.0-5.0 mm,with the correlation coefficients(r)of 0.819,0.815 and 0.818,and standard errors of prediction(SEP)of 2.29,2.41 and 2.42 mg/kg,respectively.Poor N prediction model was obtained when the soil was kept in its natural moisture content with r of 0.575 and SEP of 3.275 mg/kg,compared to the performance of dried soil samples with r of 0.815 and SEP of 2.425 mg/kg.
