Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise ir...Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling.However,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC.At present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes.In this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest China.Static and dynamic environmental variables were prepared with regard to the complex hydrological processes.The widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to DBN.The hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,i.e.June to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in practice.The sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture estimations.Calculated with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods.The current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms.Moreover,because modeling soil moisture by using environmental variables is gaining increasing popularity,DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.展开更多
South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their ur...South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their urban counterparts of this region.In order to characterize the current acid rain status and identify its possible sources in the rural area of South China,we analyzed precipitation collected event by event from a rural forested watershed in southern Anhui Province between March 2007 and February 2010.The results showed that the concentrations of major ions within precipitation in the studied rural area were significantly lower than those reported for the urban areas of the same latitude in China.Nevertheless,the precipitation acidity(with an average pH value of 4.49) and the frequency of acid rain(95%) were considerably high.The relatively high ratio of(SO42+ NO 3)/(Ca2+ +NH4+) was the main cause of acid rain in this rural area,as SO 2 and NO x were the main precursors of acid rain,while Ca2+ and NH4+acted as the dominant neutralizers to the acidity.Source identification indicated that Ca2+ and Mg2+ mainly were derived from alkaline dust,SO42,NO 3 and NH4+originated mainly from anthropogenic sources such as industrial and agricultural activities,most Na +,Cl,K + and some of Mg2+ were derived from the sea.The results suggested that the major ions within precipitation in the rural area of South China were related to the meso-scale and long-range transport of particles and aerosols in the air.展开更多
Knowledge of soil carbon（C） distribution and its relationship with the environment can improve our understanding of its biogeochemical cycling and help to establish sound regional models of C cycling. However, such ...Knowledge of soil carbon（C） distribution and its relationship with the environment can improve our understanding of its biogeochemical cycling and help to establish sound regional models of C cycling. However, such knowledge is limited in environments with complex landscape configurations. In this study, we investigated the vertical distribution and storage of soil organic carbon（SOC） and soil inorganic carbon（SIC） in the 10 representative landscapes（alpine meadow, subalpine shrub and meadow, mountain grassland, mountain forest, typical steppe, desert steppe, Hexi Corridor oases cropland, Ruoshui River delta desert, Alxa Gobi desert, and sandy desert） with contrasting bioclimatic regimes in the Heihe River Basin, Northwest China. We also measured the 87 Sr/86 Sr ratio in soil carbonate to understand the sources of SIC because the ratio can be used as a proxy in calculating the contribution of pedogenic inorganic carbon（PIC） to total SIC. Our results showed that SOC contents generally decreased with increasing soil depth in all landscapes, while SIC contents exhibited more complicated variations along soil profiles in relation to pedogenic processes and parent materials at the various landscapes. There were significant differences of C stocks in the top meter among different landscapes, with SOC storage ranging from 0.82 kg C/m^2 in sandy desert to 50.48 kg C/m^2 in mountain forest and SIC storage ranging from 0.19 kg C/m^2 in alpine meadow to 21.91 kg C/m^2 in desert steppe. SIC contributed more than 75% of total C pool when SOC storage was lower than 10 kg C/m^2, and the proportion of PIC to SIC was greater than 70% as calculated from Sr isotopic ratio, suggesting the critical role of PIC in the C budget of this region. The considerable variations of SOC and SIC in different landscapes were attributed to different pedogenic environments resulted from contrasting climatic regimes, parent materials and vegetation types. This study provides an evidence for a general trade-off pattern between SOC and SIC, showing the compensatory effects of environmental conditions（especially climate） on SOC and SIC formation in these landscapes. This is largely attributed to the fact that the overall decrease in temperature and increase in precipitation from arid deserts to alpine mountains simultaneously facilitate the accumulation of SOC and depletion of SIC.展开更多
The main aim of this paper was to calculate soil organic carbon stock(SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China.A novel prediction process ...The main aim of this paper was to calculate soil organic carbon stock(SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China.A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons(MCV).The model-then-calculate with fixed-thickness(MCF),soil profile statistics(SPS),pedological professional knowledge-based(PKB) and vegetation type-based(Veg) methods were carried out for comparison.With respect to the similar pedological information,nine common layers from topsoil to bedrock were grouped in the MCV.Validation results suggested that the MCV method generated better performance than the other methods considered.For the comparison of polygon based approaches,the Veg method generated better accuracy than both SPS and PKB,as limited soil data were incorporated.Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions.The intermediate product,that is,horizon thickness maps were fluctuant enough and reflected many details in space.The linear mixed model indicated that mean annual air temperature(MAAT) was the most important predictor for the SOCS simulation.The minimal residual of the linear mixed models was achieved in the vegetation type-based model,whereas the maximal residual was fitted in the soil type-based model.About 95% of SOCS could be found in Argosols,Cambosols and Isohumosols.The largest SOCS was found in the croplands with vegetation of Triticum aestivum L.,Sorghum bicolor(L.) Moench,Glycine max(L.) Merr.,Zea mays L.and Setaria italica(L.) P.Beauv.展开更多
基金supported by the National Natural Science Foundation of China (41130530,91325301,41401237,41571212,41371224)the Jiangsu Province Science Foundation for Youths (BK20141053)the Field Frontier Program of the Institute of Soil Science,Chinese Academy of Sciences (ISSASIP1624)
文摘Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling.However,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC.At present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes.In this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest China.Static and dynamic environmental variables were prepared with regard to the complex hydrological processes.The widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to DBN.The hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,i.e.June to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in practice.The sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture estimations.Calculated with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods.The current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms.Moreover,because modeling soil moisture by using environmental variables is gaining increasing popularity,DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.
基金supported by the National Natural Science Foundation of China(Nos. 41071141 and 40601040)the International Foundation of Science (No. C/4077-2)receivedits fund from the Institute of Soil Science,Chinese Academy of Sciences (No. ISSASIP0704)
文摘South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their urban counterparts of this region.In order to characterize the current acid rain status and identify its possible sources in the rural area of South China,we analyzed precipitation collected event by event from a rural forested watershed in southern Anhui Province between March 2007 and February 2010.The results showed that the concentrations of major ions within precipitation in the studied rural area were significantly lower than those reported for the urban areas of the same latitude in China.Nevertheless,the precipitation acidity(with an average pH value of 4.49) and the frequency of acid rain(95%) were considerably high.The relatively high ratio of(SO42+ NO 3)/(Ca2+ +NH4+) was the main cause of acid rain in this rural area,as SO 2 and NO x were the main precursors of acid rain,while Ca2+ and NH4+acted as the dominant neutralizers to the acidity.Source identification indicated that Ca2+ and Mg2+ mainly were derived from alkaline dust,SO42,NO 3 and NH4+originated mainly from anthropogenic sources such as industrial and agricultural activities,most Na +,Cl,K + and some of Mg2+ were derived from the sea.The results suggested that the major ions within precipitation in the rural area of South China were related to the meso-scale and long-range transport of particles and aerosols in the air.
基金supported by the National Natural Science Foundation of China(91325301,41130530,41371224,41601221)
文摘Knowledge of soil carbon（C） distribution and its relationship with the environment can improve our understanding of its biogeochemical cycling and help to establish sound regional models of C cycling. However, such knowledge is limited in environments with complex landscape configurations. In this study, we investigated the vertical distribution and storage of soil organic carbon（SOC） and soil inorganic carbon（SIC） in the 10 representative landscapes（alpine meadow, subalpine shrub and meadow, mountain grassland, mountain forest, typical steppe, desert steppe, Hexi Corridor oases cropland, Ruoshui River delta desert, Alxa Gobi desert, and sandy desert） with contrasting bioclimatic regimes in the Heihe River Basin, Northwest China. We also measured the 87 Sr/86 Sr ratio in soil carbonate to understand the sources of SIC because the ratio can be used as a proxy in calculating the contribution of pedogenic inorganic carbon（PIC） to total SIC. Our results showed that SOC contents generally decreased with increasing soil depth in all landscapes, while SIC contents exhibited more complicated variations along soil profiles in relation to pedogenic processes and parent materials at the various landscapes. There were significant differences of C stocks in the top meter among different landscapes, with SOC storage ranging from 0.82 kg C/m^2 in sandy desert to 50.48 kg C/m^2 in mountain forest and SIC storage ranging from 0.19 kg C/m^2 in alpine meadow to 21.91 kg C/m^2 in desert steppe. SIC contributed more than 75% of total C pool when SOC storage was lower than 10 kg C/m^2, and the proportion of PIC to SIC was greater than 70% as calculated from Sr isotopic ratio, suggesting the critical role of PIC in the C budget of this region. The considerable variations of SOC and SIC in different landscapes were attributed to different pedogenic environments resulted from contrasting climatic regimes, parent materials and vegetation types. This study provides an evidence for a general trade-off pattern between SOC and SIC, showing the compensatory effects of environmental conditions（especially climate） on SOC and SIC formation in these landscapes. This is largely attributed to the fact that the overall decrease in temperature and increase in precipitation from arid deserts to alpine mountains simultaneously facilitate the accumulation of SOC and depletion of SIC.
基金Under the auspices of Basic Project of State Commission of Science Technology of China(No.2008FY110600)National Natural Science Foundation of China(No.91325301,41401237,41571212,41371224)Field Frontier Program of Institute of Soil Science,Chinese Academy of Sciences(No.ISSASIP1624)
文摘The main aim of this paper was to calculate soil organic carbon stock(SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China.A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons(MCV).The model-then-calculate with fixed-thickness(MCF),soil profile statistics(SPS),pedological professional knowledge-based(PKB) and vegetation type-based(Veg) methods were carried out for comparison.With respect to the similar pedological information,nine common layers from topsoil to bedrock were grouped in the MCV.Validation results suggested that the MCV method generated better performance than the other methods considered.For the comparison of polygon based approaches,the Veg method generated better accuracy than both SPS and PKB,as limited soil data were incorporated.Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions.The intermediate product,that is,horizon thickness maps were fluctuant enough and reflected many details in space.The linear mixed model indicated that mean annual air temperature(MAAT) was the most important predictor for the SOCS simulation.The minimal residual of the linear mixed models was achieved in the vegetation type-based model,whereas the maximal residual was fitted in the soil type-based model.About 95% of SOCS could be found in Argosols,Cambosols and Isohumosols.The largest SOCS was found in the croplands with vegetation of Triticum aestivum L.,Sorghum bicolor(L.) Moench,Glycine max(L.) Merr.,Zea mays L.and Setaria italica(L.) P.Beauv.
