Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an ...Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.展开更多
Taking the Cambrian Yuertus Formation outcrop profiles in the Aksu-Keping-Wushi areas of northwestern Tarim Basin as examples, the depositional environments of organic rich fine sediment were analyzed by examining the...Taking the Cambrian Yuertus Formation outcrop profiles in the Aksu-Keping-Wushi areas of northwestern Tarim Basin as examples, the depositional environments of organic rich fine sediment were analyzed by examining the outcrop profiles macroscopically and microscopically. The study reveals that:(1) The lower part of the Yuertus Formation consists of organic-rich fine sediment or thin rhythmic interbeds of organic-rich fine sediment and siliceous sediment, the formation transforms to terrigenous diamictic grain shoal and inverse grading carbonate rocks upward.(2) The thin limestone interbedded with dark shale rhythmically has inverse grading.(3) The thin-bedded siliceous rock has metasomatic residual granular texture, stromatolithic structure and cementation fabric in vugs.(4) There are iron crust layers at the top of the shallowing diamictic grain shoal, beneath which exposed karst signs, such as karrens, dissolved fissures, sack-like vugs, near surface karst(plastic) breccia, breccia inside the karst system and terrigenous clastic fillings, can be seen.(5) Both the outcrops and seismic profiles show that organic-rich fine sediments above the unconformities or exposed surfaces are characterized by overlapping. The organic-rich fine sediment of the Cambrian Yuertus Formation was deposited in the anoxic-suboxidized restricted gulf lagoon environment, and its formation was controlled by high paleoproductivity and poor oxygen exchange jointly, then a shallow-water overlapping sedimentary model has been established. The results will help enrich and improve the sedimentary theory of organic-rich fine sediments.展开更多
实施退耕还林,是控制中国水土流失、改善生态环境的有效途径。如何制定最具成本-效益的退耕还林方案,以平衡生态、经济和粮食安全之间的矛盾,是保证退耕还林工程可持续发展的关键。该研究以淮河上游的息县流域为研究区,在土地利用现状...实施退耕还林,是控制中国水土流失、改善生态环境的有效途径。如何制定最具成本-效益的退耕还林方案,以平衡生态、经济和粮食安全之间的矛盾,是保证退耕还林工程可持续发展的关键。该研究以淮河上游的息县流域为研究区,在土地利用现状的基础上,一方面通过建立分布式水文模型SWAT(soil and water assessment tool)依次模拟各子流域的退耕还林操作得到泥沙削减系数,另一方面通过GDP(gross domestic product)与土地利用现状的空间叠置分析得到各子流域进行退耕还林的GDP损失系数,据此分别构成退耕还林的减沙效益目标和经济效益目标,采用多目标遗传算法NSGA-II优化求解子流域尺度的退耕还林方案。研究结果表明:1)建立的SWAT模型对研究区径流和泥沙的模拟精度较高,Nash系数分别在0.90和0.70以上,决定系数均大于0.80,且百分比偏差均控制在-20%~20%以内,可认为SWAT模型能够用于评估退耕还林的泥沙削减效果;2)子流域泥沙削减系数范围为26.70~2675.85 t/km^(2),并表现出从上游到下游逐渐减小的趋势,说明在流域上游的河源区实施单位面积的退耕还林能够取得更好的泥沙控制效果;3)子流域GDP损失系数在空间上呈现出较大的差异性,既有子流域出现了GDP的增加也有子流域出现了GDP的减小,对比发现在行政市或县主要居民点所在的子流域进行退耕还林需要付出更大的经济代价;4)多目标优化求解得到的退耕还林方案集将人均耕地面积维持在1.04×10^(−3)~1.54×10^(−3) km^(2),明显高于粮食安全的警戒水平,同时该方案集能够在仅损失30.13%~37.67%经济产值的同时将泥沙产量削减53.54%~69.86%,并达到区域可持续发展的土壤侵蚀水平。该研究提出的基于生态减沙效益和经济效益的子流域尺度退耕还林优化方法可为流域水土保持、退耕还林工程的科学规划提供借鉴和指导。展开更多
Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, incl...Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.展开更多
基金funded in part by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals[grant number CBAS2022IRP03]the National Natural Science Foundation of China[grant number 42071312]the Hainan Hundred Special Project[grant number 31,JTT[2018]].
文摘Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.
基金Supported by the China National Science and Technology Major Project(2016ZX05004002-001)the National Natural Science Foundation of China(41602147)
文摘Taking the Cambrian Yuertus Formation outcrop profiles in the Aksu-Keping-Wushi areas of northwestern Tarim Basin as examples, the depositional environments of organic rich fine sediment were analyzed by examining the outcrop profiles macroscopically and microscopically. The study reveals that:(1) The lower part of the Yuertus Formation consists of organic-rich fine sediment or thin rhythmic interbeds of organic-rich fine sediment and siliceous sediment, the formation transforms to terrigenous diamictic grain shoal and inverse grading carbonate rocks upward.(2) The thin limestone interbedded with dark shale rhythmically has inverse grading.(3) The thin-bedded siliceous rock has metasomatic residual granular texture, stromatolithic structure and cementation fabric in vugs.(4) There are iron crust layers at the top of the shallowing diamictic grain shoal, beneath which exposed karst signs, such as karrens, dissolved fissures, sack-like vugs, near surface karst(plastic) breccia, breccia inside the karst system and terrigenous clastic fillings, can be seen.(5) Both the outcrops and seismic profiles show that organic-rich fine sediments above the unconformities or exposed surfaces are characterized by overlapping. The organic-rich fine sediment of the Cambrian Yuertus Formation was deposited in the anoxic-suboxidized restricted gulf lagoon environment, and its formation was controlled by high paleoproductivity and poor oxygen exchange jointly, then a shallow-water overlapping sedimentary model has been established. The results will help enrich and improve the sedimentary theory of organic-rich fine sediments.
文摘实施退耕还林,是控制中国水土流失、改善生态环境的有效途径。如何制定最具成本-效益的退耕还林方案,以平衡生态、经济和粮食安全之间的矛盾,是保证退耕还林工程可持续发展的关键。该研究以淮河上游的息县流域为研究区,在土地利用现状的基础上,一方面通过建立分布式水文模型SWAT(soil and water assessment tool)依次模拟各子流域的退耕还林操作得到泥沙削减系数,另一方面通过GDP(gross domestic product)与土地利用现状的空间叠置分析得到各子流域进行退耕还林的GDP损失系数,据此分别构成退耕还林的减沙效益目标和经济效益目标,采用多目标遗传算法NSGA-II优化求解子流域尺度的退耕还林方案。研究结果表明:1)建立的SWAT模型对研究区径流和泥沙的模拟精度较高,Nash系数分别在0.90和0.70以上,决定系数均大于0.80,且百分比偏差均控制在-20%~20%以内,可认为SWAT模型能够用于评估退耕还林的泥沙削减效果;2)子流域泥沙削减系数范围为26.70~2675.85 t/km^(2),并表现出从上游到下游逐渐减小的趋势,说明在流域上游的河源区实施单位面积的退耕还林能够取得更好的泥沙控制效果;3)子流域GDP损失系数在空间上呈现出较大的差异性,既有子流域出现了GDP的增加也有子流域出现了GDP的减小,对比发现在行政市或县主要居民点所在的子流域进行退耕还林需要付出更大的经济代价;4)多目标优化求解得到的退耕还林方案集将人均耕地面积维持在1.04×10^(−3)~1.54×10^(−3) km^(2),明显高于粮食安全的警戒水平,同时该方案集能够在仅损失30.13%~37.67%经济产值的同时将泥沙产量削减53.54%~69.86%,并达到区域可持续发展的土壤侵蚀水平。该研究提出的基于生态减沙效益和经济效益的子流域尺度退耕还林优化方法可为流域水土保持、退耕还林工程的科学规划提供借鉴和指导。
基金Under the auspices of National Natural Science Foundation of China(No.41201420,41130744)Beijing Nova Program(No.Z111106054511097)Foundation of Beijing Municipal Commission of Education(No.KM201110028016)
文摘Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.
