College of Resources and Environment/Sino-Danish Center, Univers Beijing 100049, China nstitute of Geographic Sciences and ty of Chinese Academy of SciencesAbstract： Quantifying the contributions of climate change a...College of Resources and Environment/Sino-Danish Center, Univers Beijing 100049, China nstitute of Geographic Sciences and ty of Chinese Academy of SciencesAbstract： Quantifying the contributions of climate change and human activities to ecosystem evapotranspiration （ET） and gross primary productivity （GPP） changes is important for adaptation assessment and sustainable development. Spatiotemporal patterns of ET and GPP were estimated from 2000 to 2014 over North China Plain （NCP） with a physical and remote sensing-based model. The contributions of climate change and human activities to ET and GPP trends were separated and quantified by the first difference de-trending method and multivariate regression. Results showed that annual ET and GPP increased weakly, with climate change and human activities contributing 0.188 mm yr-2 and 0.466 mm yr-2 to ET trend of 0.654 mm yr-2, and -1.321 g C m-2 yr-2 and 7.542 g C m-2 yr-2 to GPP trend of 6.221 g C m-2 yr-2, respectively. In cropland, the increasing trends mainly occurred in wheat growing stage; the contributions of climate change to wheat and maize were both negative. Precipitation and sunshine duration were the major climatic factors regulating ET and GPP trends. It is concluded that human activities are the main drivers to the long term tendencies of water consumption and gross primary productivity in the NCP.展开更多
Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,so...Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.展开更多
Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and ...Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the com- plex interactions among the carbon （C）, water, and nitrogen （N） cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N proc- esses. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter （SOM） decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrifica- tion/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.展开更多
Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water a...Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water and lake conditions.However,the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water.Here,we propose a new approach called dynamic water diversion optimization(DWDO),which combines a comprehensive water quality model with a deep reinforcement learning algorithm.We applied DWDO to a region of Lake Dianchi,the largest eutrophic freshwater lake in China and validated it.Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7%and 6%,respectively,compared to previous operations.Additionally,annual water diversion decreased by an impressive 75%.Through interpretable machine learning,we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion.We found that a single input variable could either increase or decrease water diversion,depending on its specific value,while multiple factors collectively influenced real-time adjustment of water diversion.Moreover,using well-designed hyperparameters,DWDO proved robust under different uncertainties in model parameters.The training time of the model is theoretically shorter than traditional simulation-optimization algorithms,highlighting its potential to support more effective decisionmaking in water quality management.展开更多
Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area m...Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.展开更多
Forests worldwide are experiencing increasingly intense biotic disturbances;however,assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions am...Forests worldwide are experiencing increasingly intense biotic disturbances;however,assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions among them.This particularly applies to invasive species,which can greatly alter ecological processes in their invaded territories.Here we focus on the pine wood nematode(PWN,Bursaphelenchus xylophilus),an invasive pathogen that has caused extensive mortality of pines in East Asia and more recently has invaded southern Europe.It is expected to expand its range into continental Europe with heavy impacts possible.Given the unknown dynamics of PWN in continental Europe,we reviewed laboratory and field experiments conducted in Asia and southern Europe to parameterize the main components of PWN biology and host-pathogen interactions in the Biotic Disturbance Engine(BITE),a model designed to implement a variety of forest biotic agents,from fungi to large herbivores.To simulate dynamically changing host availability and conditions,BITE was coupled with the forest landscape model iLand.The potential impacts of introducing PWN were assessed in a Central European forest landscape(40,928ha),likely within PWN’s reach in future decades.A parameter sensitivity analysis indicated a substantial influence of factors related to dispersal,colonization,and vegetation impact,whereas parameters related to population growth manifested a minor effect.Selection of different assumptions about biological processes resulted in differential timing and size of the main mortality wave,eliminating 40%–95%of pine trees within 100 years post-introduction,with a maximum annual carbon loss between 1.3%and 4.2%.PWN-induced tree mortality reduced the Gross Primary Productivity,increased heterotrophic respiration,and generated a distinct legacy sink effect in the recovery period.This assessment has corroborated the ecological plausibility of the simulated dynamics and highlighted the need for new strategies to navigate the substantial u展开更多
The irrigated areas in the northern region of China are important food production areas. Therefore, studies on the variability of the carbon balance in these agro-ecosystems are fundamental for the management of carbo...The irrigated areas in the northern region of China are important food production areas. Therefore, studies on the variability of the carbon balance in these agro-ecosystems are fundamental for the management of carbon sequestration. This paper simulated the long-term variability of the carbon balance in a typical irrigated area along the lower Yellow River from 1984 to 2006, using a process-based ecosystem model called the Simple Biosphere Model, version 2. The mean annual gross primary production (GPP), mean annual net assimilation rate (NAR), mean annual soil respiration (Rs ), and mean annual net ecosystem exchange (NEE) were 1733, 1642, 1304, and 338g C m-2 a-1 , respectively. A significant increasing trend in the seasonal total NAR during the wheat growing season, and a significant decreasing trend in the seasonal total NAR during the maize growing season were detected. However, no significant trend was found in the annual NAR, R s , and NEE. The average carbon sequestration was 1.93 Tg C a-1 when the grain harvest was not taken into account, and the carbon sequestration amount during the maize season was higher than that during the wheat season. However, the agro-ecosystem was a weak carbon source with a value of 0.23 Tg C a-1 , when the carbon in the grain was assumed emitted into the atmosphere.展开更多
Aims Changing climate and land use patterns make it increasingly important that the hydrology of catchments and ecosystems can be reliably characterized.The aim of this paper is to identify the biophysical factors tha...Aims Changing climate and land use patterns make it increasingly important that the hydrology of catchments and ecosystems can be reliably characterized.The aim of this paper is to identify the biophysical factors that determine the rates of water vapor loss from different types of vegetation,and to seek,from an array of currently available satelliteborne sensors,those that might be used to initialize and drive landscape-level hydrologic models.Important Findings Spatial variation in the mean heights,crowd widths,and leaf area indices(LAI)of plant communities are important structural variables that affect the hydrology of landscapes.Canopy stomatal conductance(G)imposes physiological limitation on transpiration by vegetation.The maximum value of G(Gmax)is closely linked to canopy photosynthetic capacity,which can be estimated via remote sensing of foliar chlorophyll or nitrogen contents.Gcan be modeled as a nonlinear multipliable function of:(i)leaf–air vapor pressure deficit,(ii)water potential gradient between soil and leaves,(iii)photosynthetically active radiation absorbed by the canopy,(iv)plant nutrition,(v)temperature and(vi)the CO_(2) concentration of the air.Periodic surveys with Light Detection and Ranging(LiDAR)and interferometric RADAR,along with high-resolution spectral coverage in the visible,near-infrared,and thermal infrared bands,provide,along with meteorological data gathered from weather satellites,the kind of information required to model seasonal and interannual variation in transpiration and evaporation from landscapes with diverse and dynamic vegetation.展开更多
A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling,...A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.展开更多
A process-based 3D numerical model for surfzone hydrodynamics and beach evolution was established. Comparisons between the experimental data and model results proved that the model could effectively describe the hydro...A process-based 3D numerical model for surfzone hydrodynamics and beach evolution was established. Comparisons between the experimental data and model results proved that the model could effectively describe the hydrodynamics, sediment transport feature and sandbar migration process in the surfzone with satisfactory precision. A series of numerical simulations on the wave breaking and shoaling up to a barred beach were carried out based on the model system. Analyzed from the model results, the wave-induced current system in the surfzone consists of two major processes, which are the phase-averaged undertow caused by wave breaking and the net drift caused by both of the nonlinear wave motion and surface roller effect. When storm waves come to the barred beach, the strong offshore undertow along the beach suppresses the onshore net drift, making the initial sandbar migrate to the seaside. Under the condition of calm wave environment, both the undertow and net drift flow to the shoreline at the offshore side of the sandbar, and then push the initial sandbar to the shoreline. The consideration of surface roller has significant impact on the modeling results of the sandbar migration. As the roller transfer rate increases, the sandbar moves onshore especially under the storm wave condition.展开更多
Aims Process-based models are basic tools for predicting the response of forest carbon to future climate change.The models have commonly been tested for their predictions of spatial variation in forest produc-tivity,b...Aims Process-based models are basic tools for predicting the response of forest carbon to future climate change.The models have commonly been tested for their predictions of spatial variation in forest produc-tivity,but much less for their ability to predict temporal variation.Here,we explored methods to test the models with tree rings,using BIOME-BGC as an example.Methods We used net primary productivity(NPP)data and tree rings col-lected from five major forest types along the altitudinal gradient of Mt.Changbai,northeast China,to test local-parameterized BIOME-BGC model.We first test the model’s predictions of both spatial(Test 1)and temporal changes(Test 2)in productivity.Then we test if the model can detect the climatic factors limiting forest productiv-ity during historical climate change,as revealed by dendroclimatic analyses(Test 3).Important Findings Our results showed that BIOME-BGC could well simulate NPP of five forest types on Mt.Changbai,with an r^(2) of 0.69 between mod-eled and observed NPP for 17 plots along the altitudinal gradient(Test 1).Meanwhile,modeled NPP and ring-width indices were cor-related and showed similar temporal trends for each forest type(Test 2).While these tests suggest that the model’s predictions on spatial and temporal variation of NPP were acceptable,a further test that relate the correlations of modeled NPP with climate variables to the correlations of ring widths with climate(Test 3)showed that the model did not well identify the climatic factors limiting historical productivity dynamics for some forest types,and thus cannot reli-ably predict their future.Both dendrochronology and BIOME-BGC showed that forest types differed markedly in the climate factors limiting productivity because of differences in tree species and cli-mate condition,and thus differed in responses to climate change.Our results showed that a successful prediction of spatial NPP pat-terns cannot assure that BIOME-BGC can well simulate histori-cal NPP dynamics.Further,a correlation between modeled NP展开更多
Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental...Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.展开更多
基金National Natural Science Foundation of China, No.41471026 National Key Research and Development Program of China, No.2016YFC0401402Acknowledgment We thank to all the data providers. We also appreciate editors and reviewers for their constructive comments and suggestions. Finally, the first author is grateful to the invaluable support received from doctoral student ZOU Yi.
文摘College of Resources and Environment/Sino-Danish Center, Univers Beijing 100049, China nstitute of Geographic Sciences and ty of Chinese Academy of SciencesAbstract： Quantifying the contributions of climate change and human activities to ecosystem evapotranspiration （ET） and gross primary productivity （GPP） changes is important for adaptation assessment and sustainable development. Spatiotemporal patterns of ET and GPP were estimated from 2000 to 2014 over North China Plain （NCP） with a physical and remote sensing-based model. The contributions of climate change and human activities to ET and GPP trends were separated and quantified by the first difference de-trending method and multivariate regression. Results showed that annual ET and GPP increased weakly, with climate change and human activities contributing 0.188 mm yr-2 and 0.466 mm yr-2 to ET trend of 0.654 mm yr-2, and -1.321 g C m-2 yr-2 and 7.542 g C m-2 yr-2 to GPP trend of 6.221 g C m-2 yr-2, respectively. In cropland, the increasing trends mainly occurred in wheat growing stage; the contributions of climate change to wheat and maize were both negative. Precipitation and sunshine duration were the major climatic factors regulating ET and GPP trends. It is concluded that human activities are the main drivers to the long term tendencies of water consumption and gross primary productivity in the NCP.
基金supported by NASA Kentucky NNX15AR69H,NSF grant nos.1940696,1903722,and 1243232Andrew Carnegie Fellowship Award no.G-F-19-56910.
文摘Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.
基金supported by the International Science & Technology Cooperation Program of China (2010DFA92720-10)the "Hundred Talents Program" of the Chinese Academy of Sciences (Y174131001)supported by the National Basic Research Program of China (2009CB825105)
文摘Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the com- plex interactions among the carbon （C）, water, and nitrogen （N） cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N proc- esses. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter （SOM） decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrifica- tion/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.
基金supported by the National Social Science Foundation of China(21AZD060),Chinathe National Natural Science Foundation of China(51721006),Chinathe High-Performance Computing Platform of Peking University,China.
文摘Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water and lake conditions.However,the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water.Here,we propose a new approach called dynamic water diversion optimization(DWDO),which combines a comprehensive water quality model with a deep reinforcement learning algorithm.We applied DWDO to a region of Lake Dianchi,the largest eutrophic freshwater lake in China and validated it.Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7%and 6%,respectively,compared to previous operations.Additionally,annual water diversion decreased by an impressive 75%.Through interpretable machine learning,we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion.We found that a single input variable could either increase or decrease water diversion,depending on its specific value,while multiple factors collectively influenced real-time adjustment of water diversion.Moreover,using well-designed hyperparameters,DWDO proved robust under different uncertainties in model parameters.The training time of the model is theoretically shorter than traditional simulation-optimization algorithms,highlighting its potential to support more effective decisionmaking in water quality management.
基金This study was supported by the National Natural Science Foundation of China （Grant No. 30471389）
文摘Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.
基金supported by the project“EVA4.0”,No.CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE of the Czech Republicthe H2020 project RESONATE under grant agreement No.101000574.
文摘Forests worldwide are experiencing increasingly intense biotic disturbances;however,assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions among them.This particularly applies to invasive species,which can greatly alter ecological processes in their invaded territories.Here we focus on the pine wood nematode(PWN,Bursaphelenchus xylophilus),an invasive pathogen that has caused extensive mortality of pines in East Asia and more recently has invaded southern Europe.It is expected to expand its range into continental Europe with heavy impacts possible.Given the unknown dynamics of PWN in continental Europe,we reviewed laboratory and field experiments conducted in Asia and southern Europe to parameterize the main components of PWN biology and host-pathogen interactions in the Biotic Disturbance Engine(BITE),a model designed to implement a variety of forest biotic agents,from fungi to large herbivores.To simulate dynamically changing host availability and conditions,BITE was coupled with the forest landscape model iLand.The potential impacts of introducing PWN were assessed in a Central European forest landscape(40,928ha),likely within PWN’s reach in future decades.A parameter sensitivity analysis indicated a substantial influence of factors related to dispersal,colonization,and vegetation impact,whereas parameters related to population growth manifested a minor effect.Selection of different assumptions about biological processes resulted in differential timing and size of the main mortality wave,eliminating 40%–95%of pine trees within 100 years post-introduction,with a maximum annual carbon loss between 1.3%and 4.2%.PWN-induced tree mortality reduced the Gross Primary Productivity,increased heterotrophic respiration,and generated a distinct legacy sink effect in the recovery period.This assessment has corroborated the ecological plausibility of the simulated dynamics and highlighted the need for new strategies to navigate the substantial u
基金supported by National Natural Science Funds for Distinguished Young Scholar (Grant No.51025931)National Natural Science Foundation of China (Grant Nos.50939004 and 50909051)China Postdoctoral Science Foundation(Grant No. 2011M500021)
文摘The irrigated areas in the northern region of China are important food production areas. Therefore, studies on the variability of the carbon balance in these agro-ecosystems are fundamental for the management of carbon sequestration. This paper simulated the long-term variability of the carbon balance in a typical irrigated area along the lower Yellow River from 1984 to 2006, using a process-based ecosystem model called the Simple Biosphere Model, version 2. The mean annual gross primary production (GPP), mean annual net assimilation rate (NAR), mean annual soil respiration (Rs ), and mean annual net ecosystem exchange (NEE) were 1733, 1642, 1304, and 338g C m-2 a-1 , respectively. A significant increasing trend in the seasonal total NAR during the wheat growing season, and a significant decreasing trend in the seasonal total NAR during the maize growing season were detected. However, no significant trend was found in the annual NAR, R s , and NEE. The average carbon sequestration was 1.93 Tg C a-1 when the grain harvest was not taken into account, and the carbon sequestration amount during the maize season was higher than that during the wheat season. However, the agro-ecosystem was a weak carbon source with a value of 0.23 Tg C a-1 , when the carbon in the grain was assumed emitted into the atmosphere.
文摘Aims Changing climate and land use patterns make it increasingly important that the hydrology of catchments and ecosystems can be reliably characterized.The aim of this paper is to identify the biophysical factors that determine the rates of water vapor loss from different types of vegetation,and to seek,from an array of currently available satelliteborne sensors,those that might be used to initialize and drive landscape-level hydrologic models.Important Findings Spatial variation in the mean heights,crowd widths,and leaf area indices(LAI)of plant communities are important structural variables that affect the hydrology of landscapes.Canopy stomatal conductance(G)imposes physiological limitation on transpiration by vegetation.The maximum value of G(Gmax)is closely linked to canopy photosynthetic capacity,which can be estimated via remote sensing of foliar chlorophyll or nitrogen contents.Gcan be modeled as a nonlinear multipliable function of:(i)leaf–air vapor pressure deficit,(ii)water potential gradient between soil and leaves,(iii)photosynthetically active radiation absorbed by the canopy,(iv)plant nutrition,(v)temperature and(vi)the CO_(2) concentration of the air.Periodic surveys with Light Detection and Ranging(LiDAR)and interferometric RADAR,along with high-resolution spectral coverage in the visible,near-infrared,and thermal infrared bands,provide,along with meteorological data gathered from weather satellites,the kind of information required to model seasonal and interannual variation in transpiration and evaporation from landscapes with diverse and dynamic vegetation.
文摘A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.
基金financially supported by the National Key Research and Development Program of China(Grant No.2016YFC0402603)the National Natural Science Foundation of China(Grant Nos.51779112,51509119,and 51609029)+2 种基金the Project of Tianjin Natural Science Foundation(Grant No.16JCQNJC06900)the Open Project of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(Grant No.2014492211)the Fundamental Research Funds for the Central Public Welfare Research Institutes(Grant Nos.TKS170101and TKS170202)
文摘A process-based 3D numerical model for surfzone hydrodynamics and beach evolution was established. Comparisons between the experimental data and model results proved that the model could effectively describe the hydrodynamics, sediment transport feature and sandbar migration process in the surfzone with satisfactory precision. A series of numerical simulations on the wave breaking and shoaling up to a barred beach were carried out based on the model system. Analyzed from the model results, the wave-induced current system in the surfzone consists of two major processes, which are the phase-averaged undertow caused by wave breaking and the net drift caused by both of the nonlinear wave motion and surface roller effect. When storm waves come to the barred beach, the strong offshore undertow along the beach suppresses the onshore net drift, making the initial sandbar migrate to the seaside. Under the condition of calm wave environment, both the undertow and net drift flow to the shoreline at the offshore side of the sandbar, and then push the initial sandbar to the shoreline. The consideration of surface roller has significant impact on the modeling results of the sandbar migration. As the roller transfer rate increases, the sandbar moves onshore especially under the storm wave condition.
基金This work was supported by the National Natural Science Foundation of China(31370620 and 31321061)the State Scholarship Fund of China(2011811457).
文摘Aims Process-based models are basic tools for predicting the response of forest carbon to future climate change.The models have commonly been tested for their predictions of spatial variation in forest produc-tivity,but much less for their ability to predict temporal variation.Here,we explored methods to test the models with tree rings,using BIOME-BGC as an example.Methods We used net primary productivity(NPP)data and tree rings col-lected from five major forest types along the altitudinal gradient of Mt.Changbai,northeast China,to test local-parameterized BIOME-BGC model.We first test the model’s predictions of both spatial(Test 1)and temporal changes(Test 2)in productivity.Then we test if the model can detect the climatic factors limiting forest productiv-ity during historical climate change,as revealed by dendroclimatic analyses(Test 3).Important Findings Our results showed that BIOME-BGC could well simulate NPP of five forest types on Mt.Changbai,with an r^(2) of 0.69 between mod-eled and observed NPP for 17 plots along the altitudinal gradient(Test 1).Meanwhile,modeled NPP and ring-width indices were cor-related and showed similar temporal trends for each forest type(Test 2).While these tests suggest that the model’s predictions on spatial and temporal variation of NPP were acceptable,a further test that relate the correlations of modeled NPP with climate variables to the correlations of ring widths with climate(Test 3)showed that the model did not well identify the climatic factors limiting historical productivity dynamics for some forest types,and thus cannot reli-ably predict their future.Both dendrochronology and BIOME-BGC showed that forest types differed markedly in the climate factors limiting productivity because of differences in tree species and cli-mate condition,and thus differed in responses to climate change.Our results showed that a successful prediction of spatial NPP pat-terns cannot assure that BIOME-BGC can well simulate histori-cal NPP dynamics.Further,a correlation between modeled NP
基金This work is supported by New Zealand Ministry of Foreign Affairs and Trade PhD Scholarship and the University of Auckland’s Postgraduate Research Student SupportMinistry of Foreign Affairs and Trade,New Zealand,University of Auckland.
文摘Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.
