Estimating potential yield of wheat production in China based on cross-scale data-model fusion

The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer （DSSAT） model and the Agro- ecological Zone （AEZ） model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations （1962-1999） are employed to estimate average crop productivity. Comparison of the two models＇ estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.

Data-Model Hybrid Driven Topology Identification Framework for Distribution Networks

Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.

Multi-scale observation and cross-scale mechanistic modeling on terrestrial ecosystem carbon cycle

To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. in summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.

Spatial patterns of ecosystem carbon residence time in Chinese forests

Capacity of carbon sequestration in forest ecosystem largely depends on the trend of net primary production (NPP) and the length of ecosystem carbon residence time. Retrieving spatial patterns of ecosystem carbon residence time is important and necessary for accurately predicting regional carbon cycles in the future. In this study, a data-model fusion method that combined a process-based regional carbon model (TECO-R) with various ground-based ecosystem observations (NPP, biomass, and soil organic carbon) and auxiliary data sets (NDVI, meteorological data, and maps of vegetation and soil texture) was applied to estimate spatial patterns of ecosystem carbon residence time in Chinese forests at steady state. In the data-model fusion, the genetic algorithm was used to estimate the optimal model parameters related with the ecosystem carbon residence time by minimizing total deviation between modeled and observed values. The results indicated that data-model fusion technology could effectively retrieve model parameters and simulate carbon cycling processes for Chinese forest ecosystems. The estimated carbon residence times were highly heterogenous over China, with most of regions having values between 24 and 70 years. The deciduous needleleaf forest and the evergreen needleleaf forest had the highest averaged carbon residence times (73.8 and 71.3 years, respectively), the mixed forest and the deciduous broadleaf forest had moderate values (38.1 and 37.3 years, respectively), and the evergreen broadleaf forest had the lowest value (31.7 years). The averaged carbon residence time of forest ecosystems in China was 57.8 years.

Integrative ecology in the era of big data——From observation to prediction

Most ecological and environmental issues faced by human society can only be solved at the ecosystem,watershed,regional and even global scale.Thus,ecological research is developing rapidly towards macro-scale studies.With the rapid development of observational networks and information technology,the spaceborne-aircraft-ground based observation system is becoming an important feature of ecosystem monitoring in the new era.With the gradual formation of the global new-generation observational systems and the rapid expansion of massive multi-source heterogeneous data,ecology has entered the era of big data,big science,and big theory.How to integrate ecological big data,discover valuable ecological laws and mechanisms,and further expand them to solve eco-environmental issues that closely relate to human development are the major opportunities and challenges in this field.In this paper,we systematically summarized the research progresses in ecological big data,reviewed the opportunity and demand of integrative ecology,and further discussed the main approaches of ecological big data integration by using meta-analysis,data mining,and data-model fusion.Finally,we proposed the prospects and research directions of integrative ecology and suggested that future researches need to integrate big data into land models so as to improve the accuracy of ecological forecasting.It can be foreseen that under the background of global change and the rapid development of big data in the future,integrative ecology will be extensively applied and developed to serve the sustainable development of human society.

Observed,Reconstructed,and Simulated Decadal Variability of Summer Precipitation over Eastern China

Based on observations made during recent decades, reconstructed precipitation for the period A.D. 1736-2000, dry-wet index data for A.D. 500-2000, and a 1000-yr control simulation using the Community Earth System Model with fixed pre-industrial external forcing, the decadal variability of summer precipitation over eastem China is stud- ied. Power spectrum analysis shows that the dominant cycles for the decadal variation of summer precipitation are： 22-24 and quasi-70 yr over the North China Plain; 32-36, 44-48, and quasi-70 yr in the Jiang-Huai area; and 32-36 and 4448 yr in the Jiang-Nan area. Bandpass decomposition from observation, reconstruction, and simulation re- veals that the variability of summer precipitation over the North China Plain, Jiang-Huai area, and Jiang-Nan area, at scales of 20-35, 35-50, and 50-80 yr, is not consistent across the entire millennium. We also find that the warm （cold） phase of the Pacific Decadal Oscillation generally corresponds to dry （wet） conditions over the North China Plain, but wet （dry） conditions in the Jiang-Nan area, from A.D. 1800, when the PDO became strengthened. However, such a correspondence does not exist throughout the entire last millennium. Data-model comparison sug- gests that these decadal oscillations and their temporal evolution over eastem China, including the decadal shifts in the spatial pattem of the precipitation anomaly observed in the late 1970s, early 1990s, and early 2000s, might result from internal variability of the climate system.