USGS Science⌕ Search

Geology topics

Shuguang Liu

Publications and source records attributed to Shuguang Liu.

52 records · Page 3Linked to original sources
USGS2010

Developing a spatially distributed terrestrial biogeochemical cycle modeling system to support the management of Fort Benning and its surrounding areas

The overarching goal of this project is to develop an advanced, spatially distributed biogeochemical cycle modeling system to simulate the dynamics of ecosystem C and N cycles under historical, current, and future land use and disturbances scenarios. The modeling system and simulated results were used to facilitate the evaluation of the environmental consequences of various training and management activities and, therefore, to support future land use and training operations.

Georgia↗
USGS2009

Modeling to evaluate the response of savanna-derived cropland to warming–drying stress and nitrogen fertilizers

Many savannas in West Africa have been converted to croplands and are among the world’s regions most vulnerable to climate change due to deteriorating soil quality. We focused on the savanna-derived cropland in northern Ghana to simulate its sensitivity to projected climate change and nitrogen fertilization scenarios. Here we show that progressive warming–drying stress over the twenty-first century will enhance soil carbon emissions from all kinds of lands of which the natural ecosystems will be more vulnerable to variation in climate variables, particularly in annual precipitation. The carbon emissions from all croplands, however, could be mitigated by applying nitrogen fertilizer at 30–60 kg N ha − 1 year − 1 . The uncertainties of soil organic carbon budgets and crop yields depend mainly on the nitrogen fertilization rate during the first 40 years and then are dominated by climate drying stress. The replenishment of soil nutrients, especially of nitrogen through fertilization, could be one of the priority options for policy makers and farm managers as they evaluate mitigation and adaptation strategies of cropping systems and management practices to sustain agriculture and ensure food security under a changing climate.

Climatic Change↗
USGS2009

Effects of vegetation restoration and slope positions on soil aggregation and soil carbon accumulation on heavily eroded tropical land of Southern China

Background aim and scope Soil organic carbon (SOC) accumulation is strongly affected by soil erosion and deposition that differ at slope positions of a watershed. However, studies on the effects of topography on soil aggregation and SOC dynamics, especially after the implementation of vegetation restoration, are rare. Poorly understood mechanisms and a lack of quantification for the suite of ecological benefits brought by the impacts of topography after planting further obstructed our understanding of terrestrial ecosystem carbon (C) sequestration. The purposes of this study are to (1) quantify the impacts of vegetation restoration on size and stability of soil aggregates and the sequestration of C in soil and (2) to address the impacts of various slope locations on aggregates and SOC distribution. Materials and methods The experimental sites were set up in 1959 on a highly disturbed barren land in a tropical and coastal area of Guangdong province in South China. One site received human-induced vegetation restoration (the restored site), while the other received no planting and has remained as barren land (the barren site). The soil in the study sites was a latosol developed from granite. Soil samples were taken from 0 to 20 and 20 to 40 cm soil layer at shoulder and toe slope positions at both sites for comparisons. Soils were analyzed for proportion of soil macroaggregates (>0.25 mm), the SOC in soil layers, and the aggregate soil organic carbon (AOC) at different aggregate sizes. Results and discussion Measurements in 2007 showed that fractions of water stable macroaggregates in 0–40 cm at shoulder and toe slope ranged from 28% to 45%, about one third to one half of those of dry macroaggregates (91–95%) at the restored site. Soil macroaggregates were not detected at barren site in 2007. Average SOC storage in 0–40 cm soil layer of shoulder and toe slope positions at the restored site was 56.5 ± 10.9 Mg C ha −1 , about 2.4 times of that (23.4 ± 4.6 Mg C ha −1 ) at barren site in 2007. Since 1959, the soil aggregation and SOC storage are significantly improved at the restored site; opposite to that, soil physical and chemical quality has remained low on the barren land without planting. SOC storage in 0–40 cm at toe slope was 15.9 ± 1.8 Mg C ha −1 , which is only half of that (30.9 ± 9 Mg C ha −1 ) at shoulder slope of the barren site; this is opposite to the pattern found at restored site. The ratios of AOC in 0–20 cm to AOC in 20–40 cm at toe slope were lower than those at shoulder slope of the restored site. The comparison of organic carbon sequestered in soils at different slope positions suggest that soil aggregates played a role in sequestering C based upon landscape positions and soil profile depth as a consequence of soil erosion and deposition. Conclusions Results indicate that vegetation restoration and SOC accumulation significantly enhance soil aggregation, which in turn promotes further organic C accumulation in the aggregates via physical protection. Soil aggregation and soil C accumulation differed between slope positions. Soil aggregation was significantly enhanced in 0–20 cm layer and aggregates absorb C into deep layers in depositional environment (toe slope) under protection from human disturbances. The interactions of erosion–deposition, soil aggregates, and vegetation restoration play important roles on SOC accumulation and redistribution on land. Recommendations and perspectives The positive feedback between SOC and soil aggregates should be evaluated for improving the quantification of the impacts of land use change, erosion, and deposition on the dynamics of SOC and soil structure under the global climate change.

Guandong Province↗
USGS2009

Methodology for an integrative assessment of China's ecological restoration programs

While research projects have been conducted to examine the impacts and effectiveness of China's ecological restoration programs, few of them represent integrated, systematic efforts. The objective of this chapter is thus to articulate and outline a methodology for an integrative assessment, which, we believe, should embrace both the environmental and socioeconomic changes and engage investigations at multiple scales. Further, these investigations should be pursued through interdisciplinary collaboration with expertise from ecology, economics, hydrology, and geospatial, climate, and land change sciences. We argue that the deployment of geospatial capability, the use of longitudinal data, and the connection between science and policy should be the hallmarks of an integrative assessment. We also describe our general approach and specific models to quantify the environmental and socioeconomic impacts induced by implementing the restoration programs, and address the issue of how to overcome the challenges in generating the data needed for executing various empirical tasks. We hope that the adoption and application of this methodology will make a valuable contribution to a more robust and timely assessment as well as implementation of the ecological restoration programs in and outside of China.

Book chapter↗
USGS2009

Quantifying terrestrial ecosystem carbon dynamics in the Jinsha watershed, Upper Yangtze, China from 1975 to 2000

Quantifying the spatial and temporal dynamics of carbon stocks in terrestrial ecosystems and carbon fluxes between the terrestrial biosphere and the atmosphere is critical to our understanding of regional patterns of carbon storage and loss. Here we use the General Ensemble Biogeochemical Modeling System to simulate the terrestrial ecosystem carbon dynamics in the Jinsha watershed of China's upper Yangtze basin from 1975 to 2000, based on unique combinations of spatial and temporal dynamics of major driving forces, such as climate, soil properties, nitrogen deposition, and land use and land cover changes. Our analysis demonstrates that the Jinsha watershed ecosystems acted as a carbon sink during the period of 1975–2000, with an average rate of 0.36 Mg/ha/yr, primarily resulting from regional climate variation and local land use and land cover change. Vegetation biomass accumulation accounted for 90.6% of the sink, while soil organic carbon loss before 1992 led to lower net gain of carbon in the watershed, and after that soils became a small sink. Ecosystem carbon sinks/source pattern showed a high degree of spatial heterogeneity, Carbon sinks were associated with forest areas without disturbances, whereas carbon Sources were primarily caused by stand-replacing disturbances. This highlights the importance of land-use history in determining the regional carbon sinks/source pattern.

Jinsha watershed, Yangtze River↗
USGS2009

Distributed geospatial model sharing based on open interoperability standards

Numerous geospatial computational models have been developed based on sound principles and published in journals or presented in conferences. However modelers have made few advances in the development of computable modules that facilitate sharing during model development or utilization. Constraints hampering development of model sharing technology includes limitations on computing, storage, and connectivity; traditional stand-alone and closed network systems cannot fully support sharing and integrating geospatial models. To address this need, we have identified methods for sharing geospatial computational models using Service Oriented Architecture (SOA) techniques and open geospatial standards. The service-oriented model sharing service is accessible using any tools or systems compliant with open geospatial standards, making it possible to utilize vast scientific resources available from around the world to solve highly sophisticated application problems. The methods also allow model services to be empowered by diverse computational devices and technologies, such as portable devices and GRID computing infrastructures. Based on the generic and abstract operations and data structures required for Web Processing Service (WPS) standards, we developed an interactive interface for model sharing to help reduce interoperability problems for model use. Geospatial computational models are shared on model services, where the computational processes provided by models can be accessed through tools and systems compliant with WPS. We developed a platform to help modelers publish individual models in a simplified and efficient way. Finally, we illustrate our technique using wetland hydrological models we developed for the prairie pothole region of North America.

Journal of Remote Sensing↗
USGS2007

Estimating soil erosion using the USPED model and consecutive remotely sensed land cover observations

Intensified soil erosion contributes to the degradation of ecosystems. Better estimation of soil erosion across landscapes is a necessary part of understanding ecosystem biogeochemical cycles and ecosystem sustainability. In this study, we used the Unit Stream Power-based Erosion Deposition (USPED) model to estimate the lateral movement of soils across Fort Benning, a military training installation in western Georgia, USA. A land cover weight factor was used in the calculation of surface flow accumulation. The simulation results were compared with observations of the total suspended sediments in stream water for ten watersheds, and showed a significant linear relationship (R 2 = 0.72). Erosion estimates of the ten watersheds are also related to the land disturbance index that is a measure of the intensity of military training disturbances. Results suggest that the USPED model is an effective tool to quantify erosion and deposition at military installations.

Georgia↗
USGS2006

State-parameter estimation of ecosystem models using a smoothed ensemble Kalman filter

Much of the effort in data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be reduced if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) to estimate simultaneously the system states and model parameters of an eddy flux partition model. The approach is used to assimilate observed fluxes of carbon and major driving forces at an AmeriFlux forest station: Howland, Maine, USA. The aim of applying a kernel-smoothing algorithm to an ensemble Kalman filter is to overcome the dramatic, sudden change of parameter values in time and the loss of continuity between two consecutive points in time. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and so on, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving understanding and quantification of carbon cycle parameters and processes.

Conference Paper↗
USGS2006

Dealing with uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in northern forest ecosystems

Many process-based models on carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems. These models are widely used to evaluate the long-term decisions in forest management dealing with effects like particulate pollution, productivity and climate change. Regarding climate change, one of the key questions that have sensitive political implications is whether northern forests will sequester atmospheric C or not. Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion. In general, models differ in their underlying attempts to match natural complexities with assumed or imposed model structure and process formulations to estimate model parameters, to gather data and to address issues on scope, scale and natural variations. Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises. Model structure relates to the mathematical representation of the processes modelled and the type of state variables that a model contains. The modelling of partitioning among above- and below-ground C and N pools and the interdependence among these pools remain a major source of uncertainty in model structure and error propagation. Most soil C models use at least three state variables to represent the different types of soil organic matter (SOM). This approach results in creating three artificial SOM pools, assuming that each one contains C compounds with same turnover rate. In reality, SOM consists of many different types of C compounds with widely different turnover rates. Uncertainty in data and parameter estimates are closely linked. Data uncertainties are associated with high variations in estimating forest biomass, productivity and soil organic matter and may be incomplete for model initialization, calibration, validation and sensitivity analysis of generalized predictor models. The scale at which a model is being used also affects the level of uncertainty, as the errors in the prediction of the C and N dynamics differ from the site to the landscape levels and across climatic regions. If the spatial or temporal scale of a model application is changed, additional uncertainty arises from neglecting natural variability in system variables in time and space. Uncertainty issues are also intimately related to model validation and sensitivity analysis. The estimation of uncertainties is needed to inform decision process, in order to detect the possible corridor of development. Uncertainty in this context is an essential measure of quality for stakeholder and decision makers.

Conference Paper↗
USGS2000

The Kyoto protocol and payments for tropical forest: An interdisciplinary method for estimating carbon-offset supply and increasing the feasibility of a carbon market under the CDM

Protecting tropical forests under the Clean Development Mechanism (CDM) could reduce the cost of emissions limitations set in Kyoto. However, while society must soon decide whether or not to use tropical forest-based offsets, evidence regarding tropical carbon sinks is sparse. This paper presents a general method for constructing an integrated model (based on detailed historical, remote sensing and field data) that can produce land-use and carbon baselines, predict carbon sequestration supply to a carbon-offsets market and also help to evaluate optimal market rules. Creating such integrated models requires close collaboration between social and natural scientists. Our project combines varied disciplinary expertise (in economics, ecology and geography) with local knowledge in order to create high-quality, empirically grounded, integrated models for Costa Rica.

Ecological Economics↗
USGSDate not supplied

Development of stochastic modeling systems using deterministic models and GIS: Principles and a case study in the Atlantic Zone of Costa Rica

The most important requirements for large-area environmental modeling are a tight integration between models and data, and a close match of the spatial scale at which the model is developed with the scale at which the model is to be applied. To better match the scale of data with that of the model, we propose a set of principles for the development of stochastic modeling systems based on linkage of deterministic models with GIS data. For modeling purposes, a region is usually rasterized into cells and the environmental conditions of those cells are specified by ranges or classes using GIS data layers. It is not necessary to simulate each and every GIS cell in the study area because many cells may have similar environmental conditions and can be grouped together to form cohorts. We define a cohort as the assembly of the cells sharing a unique combination of environmental conditions within the study region. Multiple model simulations can be performed for any given cohort. For each simulation, some of the parameter values can be randomly generated within the specified environmental conditions of the cohort according to a certain statistical distribution which, in turn, can be specified by GIS data layers. By this method the variance and covariance of environmental variables in space and time are integrated into the simulation processes with these modeling systems to make full use of the available data and to assess the uncertainties of the simulated results. An integrated simulation system between CENTURY model and GIS was developed to demonstrate the value of the concepts imbedded in stochastic simulation systems for large area studies.

Conference Paper↗