Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China

Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO₂ concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO₂ dry air mole fraction (XCO₂) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO₂ changes during growing season for 13 years. We derived a relationship between XCO₂ and surface net CO₂ fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO₂ flux enhancement based on satellite observed XCO₂ changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO₂ emissions, we estimated an enhanced XCO₂ drawdown during the GP of −0.070 to −0.084 ppm yr⁻¹. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO₂ to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord

Analysis of Vegetation Vulnerability Dynamics and Driving Forces to Multiple Drought Stresses in a Changing Environment

Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration. However, the vulnerability of vegetation and its dynamics in a changing environment are still unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To this end, this study explored the changes of vegetation vulnerability characteristics under various drought stresses in Xinjiang and conducted quantitative attribution analysis using the random forest method. In addition, the effects of ecological water transport and increased irrigation areas on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI), standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index (SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply stress, respectively. The results showed that: (1) different vegetation types responded differently to water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation growth and near-surface temperature being the main drivers, while increased soil moisture in the root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas, which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought was reduced

Southeast Asia must narrow down the yield gap to continue to be a major rice bowl

Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps

China's food production under water and land limitations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 175-178).The future availability of the natural resources (water and land) needed for food production is highly uncertain. Evidence shows diminishing natural resources and growing food demand throughout many parts of the world. China is one of the countries that face the challenge of managing its finite water and land resources to support their population. Difficulties mainly arise from: (1) the geographic mismatch between the location of water resources and available land; (2) a large and growing number of population; and (3) limited natural resources per capita. This thesis presents a systematic approach to evaluate the effects of water and land constraints on food production and applies it to China as a case study. Based on the basic principle of water and land balance, crop resource requirements, and per capita consumption, the assessment of natural resources limitations on food production can be formulated into an optimization model, with the objective function maximizing the number of people fed subject to resource constraints. This formulation makes it possible to systematically and efficiently evaluate the effects of natural resource constraints for such a complex and large scale study regions such as China. Even though our approach is based on the basic principle, we incorporate several significant features into the model to realistically represent the spatial and temporal heterogeneity in climate, land use, and crop requirements. Our analysis is conducted at a detailed spatial resolution of 0.5' by 0.5', includes water movement at the same resolution, accommodates the mixture of crops in people's diet, and distinguishes irrigated from rain-fed agriculture. Our optimization model presents an average long term analysis. The model is developed and calibrated to reproduce long-term observed conditions during the nominal period of 1990- 2000. We then use the model together with globally and locally available data to make future predictions of China's food production capacity during the future period of 2046-2065. These future predictions include the impacts of the South-to-North Water Diversion project and projected climate change. The future climate scenarios are taken from the general circulation model predictions and represent diverse seasonal and regional patterns. Regionally, land is a limiting factor in the south, while water is a limiting factor in the north. Our results suggest that irrigation and multiple-cropping are keys in enhancing China's food production capacity to support increasing population. The spatial and seasonal distribution of rainfall changes is critical for agriculture in meeting future food requirements under climate change.by Piyatida Hoisungwan.Ph.D

Development of a 10-year (2001-2010) 0.1° data set of land-surface energy balance for mainland China

© Author(s) 2014. In the absence of high-resolution estimates of the components of surface energy balance for China, we developed an algorithm based on the surface energy balance system (SEBS) to generate a data set of land-surface energy and water fluxes on a monthly timescale from 2001 to 2010 at a 0.1 x 0.1° spatial resolution by using multi-satellite and meteorological forcing data. A remote-sensing-based method was developed to estimate canopy height, which was used to calculate roughness length and flux dynamics. The landsurface flux data set was validated against "ground-truth" observations from 11 flux tower stations in China. The estimated fluxes correlate well with the stations' measurements for different vegetation types and climatic conditions (average bias = 11.2 Wm-2, RMSE = 22.7 Wm-2). The quality of the data product was also assessed against the GLDAS data set. The results show that our method is efficient for producing a high-resolution data set of surface energy flux for the Chinese landmass from satellite data. The validation results demonstrate that more accurate downward long-wave radiation data sets are needed to be able to estimate turbulent fluxes and evapotranspiration accurately when using the surface energy balance model. Trend analysis of land-surface radiation and energy exchange fluxes revealed that the Tibetan Plateau has undergone relatively stronger climatic change than other parts of China during the last 10 years. The capability of the data set to provide spatial and temporal information on water-cycle and land-atmosphere interactions for the Chinese landmass is examined. The product is free to download for studies of the water cycle and environmental change in China

Terrestrial plant productivity and soil moisture constraints

Dolman, A.J. [Promotor]Jeu, R.M.H. de [Copromotor]Werf-, G.R. van der [Copromotor

Environmental Livelihood Security in Southeast Asia and Oceania: A Water-Energy-Food-Livelihoods Nexus Approach for Spatially Assessing Change

This document addresses the need for explicit inclusion of livelihoods within the environment nexus (water-energy-food security), not only responding to literature gaps but also addressing emerging dialogue from existing nexus consortia. We present the first conceptualization of ‘environmental livelihood security’, which combines the nexus perspective with sustainable livelihoods. The geographical focus of this paper is Southeast Asia and Oceania, a region currently wrought by the impacts of a changing climate. Climate change is the primary external forcing mechanism on the environmental livelihood security of communities in Southeast Asia and Oceania which, therefore, forms the applied crux of this paper. Finally, we provide a primer for using geospatial information to develop a spatial framework to enable geographical assessment of environmental livelihood security across the region. We conclude by linking the value of this research to ongoing sustainable development discussions, and for influencing policy agenda

Agricultural land systems : modelling past, present and future regional dynamics

This thesis arises from the understanding of how the integration of concepts, tools, techniques, and methods from geographic information science (GIS) can provide a formalised knowledge base for agricultural land systems in response to future agricultural and food system challenges. To that end, this thesis focuses on understanding the potential application of GIS-based approaches and available spatial data sources for modelling regional agricultural land-use and production dynamics in Portugal. The specific objectives of this thesis are addressed in seven chapters in Parts II through V, each corresponding to one scientific article that was either published or is being considered for publication in peer-reviewed international scientific journals. In Part II, Chapter 2 summarises the body of knowledge and provides the context for the contribution of this thesis within the scientific domain of agricultural land systems. In Part III, Chapters 3 and 4 explore remotely sensed and Volunteered Geographic Information (VGI) data, multitemporal and multisensory approaches, and a variety of statistical methods for mapping, quantifying, and assessing regional agricultural land dynamics in the Beja district. In Part IV, Chapters 5–7 explore the CA-Markov model, Markov chain model, machine learning, and model-agnostic approach, as well as a set of spatial metrics and statistical methods for modelling the factors and spatiotemporal changes of agricultural land use in the Beja district. In Part V, Chapter 8 explores an area-weighting GIS-based technique, a spatiotemporal data cube, and statistical methods to model the spatial distribution across time for regional agricultural production in Portugal. The case studies in the thesis contribute practical and theoretical knowledge by demonstrating the strengths and limitations of several GIS-based approaches. Together, the case studies demonstrate the underlying principles that underpin each approach in a way that allows us to infer their potentiality and appropriateness for modelling regional agricultural land-use and production dynamics, stimulating further research along this line. Generally, this thesis partly reflects the state-of-art of land-use modelling and contribute significantly to the introduction of advances in agricultural system modelling research and land-system science

Understanding and predicting mosquito-borne disease under current and future scenarios of global change

There is a rapidly growing awareness of the influence of global change processes such as land-use, climate change and socioeconomic factors on the burden of mosquito-borne disease (MBD). Although individual effects of different processes on MBD risk have been studied widely, a holistic approach that considers the combined influence of different global change processes has rarely been implemented. Here, I evaluate the effects of different global change processes on MBD risk, both generally, and in a series of modelling studies using the understudied MBD, Japanese encephalitis (JE) as a case study. I integrate different data types and approaches from ecology and epidemiology, with the aim of informing public health decision-makers in the era of accelerating global change. Firstly, I synthesise current knowledge on relative and interacting effects of global change processes on MBD risk and examine how these factors have been incorporated into existing analyses, highlighting how future research could be improved. Secondly, I compile a vector surveillance database for the predominant vector of JE (Culex tritaeniorhynchus). I use a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance over large spatial scales, that has the potential to be used to provide guidance for the targeting of suitable interventions. I use this information in an epidemiological study of JE case surveillance data and show that human JE incidence is associated with climate, land-use and socioeconomic factors, and these factors can be used to predict JE outbreaks in north-eastern India. Thirdly, I examine possible trends in JE epidemiology by projecting into the future under various scenarios of global change to show divergence in JE risk and burden under different socioeconomic and environmental policy scenarios. Finally, I integrate the implications of these results into our understanding of the effects of global change processes on MBD, the epidemiology and control of JE, and a holistic approach to the understanding and prediction of MBD risk