A Process Model Based Ecosystem Assessment and Management System

Feng Zhang, Jiansheng Ye, Guojun Sun Institute of Arid Agroecology School of Life Sciences

State Key Laboratory of Pastoral Agricultural Ecosystem Lanzhou University

Lanzhou, Gansu, China, 730000 Zhangfeng@lzu.edu.cn

Abstract—The Loess Plateau covers an area of 640,000 km² in the upper and middle reaches of China's Yellow River. The natural environment of this region is complex and variable and is considered as one of the major ecologically fragile areas in China. In the last two decades, with dramatic changes in population, agricultural cultivation patterns and regional climate, uncertainty surrounding the potential to achieve a sustainable agro-ecosystem in this region has increased. The agro-ecosystem conservation is the dominant challenge in the light of the complex ecological condition; significant spatial heterogeneity and serious ecological degradation.

Ecological modeling is an effective tool in the quest to solve ecological problems. Scenario simulation is an important way of tracing nutrient dynamics and optimizing management practices for an ecosystem. From the temporal and spatial perspectives, however, relying solely on historical data will lead to a certain delay in the management policy research. Particularly, more errors will be introduced in the simulated results due to the ecosystem factors, which fluctuate frequently with time, and increasing uncertainty of the whole system.

Considering the challenges aforementioned, an ecosystem assessment and management (EAM) system was set up based on process model, wireless sensor, and Geographic Information System (GIS) technologies. The system uses a mobile GIS platform for real-time data collection, and a combination of weather information and soil properties are used as inputs for the process based model to predict soil nutrients, moisture, greenhouse gas emissions, yield, etc. The EAM systems use historical data to initialize and the actual data to optimize the simulations. Real-time information was used to calibrate the simulation results, optimizing the simulation parameters to improve the accuracy of the system simulation results. The system can be used to evaluate the impact of different management methods on the ecological system and identify best management practices.

Currently, this system has been applied in the Loess Plateau to assess different management practices impact on soil carbon, nitrogen and crop yield. The EAM platform provides important information for the ecosystem management and protection.

Keywords:; Loess Plateau; GIS; EAM; Process Model; Ecological model;

I. INTRODUCTION The Loess Plateau of China, located in the Midwest, is

China's main ecologically vulnerable area. In the last 30 years, significant population increases have strained the region’s ecological system. In order to promote sustainable development of the ecological system, the area implemented a series of ecological system engineering efforts. However, the broad expanse and significant heterogeneity of the Loess Plateau has complicated local ecological system management policy development. In order to create effective policies, it is necessary to gain a better understanding of the complex ecosystem interactions specific to the area. Therefore, establishing an ecological assessment platform that integrates data from multiple sources (e.g. population, socio-economic, climate, and soil information), analyzes and simulates ecological processes, and releases ecosystem information to policy makers, experts or citizens, is an important step in evaluating the efficacy of ecological management strategies and optimizing the ecosystem management.

This research aims to establish an ecosystem assessment and management platform using various technologies, including a wireless sensor network, a process based ecological model, a GIS/RS spatial database, and a mobile information release platform. The system can provide real time soil information and management scenario simulations, making it an important decision support tool for optimizing ecosystem management practices.

II. DATA AND METHODS

A. Data Climate, soil, vegetation and agricultural management data

are collected from climatological stations, field samplings and soil sensors. Geographic Information System (GIS) software was used to map the collected climate, soil and land use type data. Counties were used as the basic unit for the database. The database consisted of: (1) dominant crop types on the Loess Plateau with plant properties (e.g. maximal production, maximal height, and root/shoot ratio); (2) soil properties (e.g. maximum and minimum soil organic carbon (SOC) content, bulk density, clay fraction and pH); (3) daily climate data (e.g. maximum and minimum air temperatures and precipitation);

Climate Soil Managment

Model

parameterization and

simulation

Current Situation

Management Scenarios

Soil organic carbon

Greenhouse gases emission

Productivity

GIS-Datebase

Integrated assessment

High productivity and low emission management

Figure 1. concept of The EAM

and (4) areas and geographic locations of major crop types at the county level.

B. Ecosystem assessment and management (EAM) framework Three indices, crop productivity, soil organic carbon

(SOC), and greenhouse gas emissions, were selected to assess the vulnerability of the ecosystem. In the EAM system, it can be simulated using the climate, soil and different management scenarios (Figure 1). As the largest terrestrial carbon pool on earth, SOC can affect soil nutrients and moisture capacity and decrease soil erosion and degradation. The amount of SOC in an ecosystem directly impacts productivity, thus the food production. Moreover, the soil’s ability to sequester carbon directly influences its contribution to global climate change. Therefore, increasing soil carbon sequestration is an effective way to reduce the atmospheric greenhouse gases [1]. SOC changes and greenhouse gas emissions have drawn increasing attention from many environmentalists and decision makers as they have significant consequences for food security and global climate change.

C. System framework The system uses a four-tiered architecture (Figure 2). (1)

The ecological monitoring and data collection system supported by a local GIS-based database and ecological sensor data; (2) The database integration system, which can assimilate multi-source data for the simulation system; (3) The ecosystem process simulation system; (4) The information publication system, which supported by PCs as well as mobile clients such as smart phones and tablets.

1) Data Collection System (DCS) DCS is the integral component of the EAM system, which

can automatically collect the soil temperature and moisture data from the climate station monitoring as well as the remote sensing data and the information from real-time sensors. DCS can integrate all the data to the EAM system simultaneously with wired or wireless techniques. In order to obtain real-time soil and climate parameters, data in the DCS are transmitted wirelessly via the Internet (Figure 3).

Figure 2. The architecture of EAM system

Figure 3. Data collection system

2) Database integration system Many data types are adopted in this research, including the

precipitation, temperature, soil, terrain, crop maps, irrigation maps, agricultural management, administrative boundaries, etc. Moreover, the data is in different structures, such as the roster data, the vector data and some agricultural management data with text descriptions. In the respect of multiple administrative departments involved in the agricultural activities and the variety of databases and data formats, a database system is established focusing on the ecological processes’ simulation. Therefore, more efficient integration of multi-source data (multi-sensor, multi-scale and multi-temporal resolution) is achieved by transmitting the relevant information and omitting the irrelevant data. The database synthesized system not only takes advantage of the wireless Internet and reduces the difficulties of data integration brought by traditional data communication methods, but also standardizes the multi-source data and ensures confidence in the data.

3) Ecosystem process simulation system(EPSS): The EPSS is the core of the EAM system. A process based

biochemical model, DNDC was used as the ecological process simulation module in the system. It is an effective way to trace the detailed ecosystem change dynamics under different management scenarios. At a national scale, process-based models have proven useful in reducing uncertainty and helping understand the complex biogeochemical processes involved in ecosystems. In order to assure the accuracy of the simulation in this research, daily climate data is used as the model driver. The EPSS uses daily soil moisture, temperature, precipitation, and real-time soil sensor data to simulate the growth of crops in

the research area and evaluate the future impact of management practices. Particular scenarios can be simulated in EPSS with deficiencies of fertilizers or water in order to optimize agricultural management practices.

The EPSS model 1) is based on the process model and cored with the crop growth submodel, the hydrothermal cycle submodel and the submodel of nutrient balance in the soil; 2) models the dynamics of the crops’ requirement for nutrients; 3) projects the crops’ yield, the soil nutrients (Carbon and Nitrogen) and the greenhouse gas emissions, which quantifies the impact of agricultural management practice on the ecological system.

4) Information publication system(IPS) In the process of ecological system assessment and

management processes, there is a need for real-time information about the ecological system to analyze and determine the best management practices. This places high requirements on the real-time ecosystem process evaluation and the approach to publish the results. The popularity of wireless Internet and smart phones brings the possibility to realize the instant interactions for the system. This system is a combination of traditional Internet service and current new wireless Internet technology. The IPS has a three-layer structure, namely, the front display service layer, the middle modeling and calculation service layer and the background database service layer. Furthermore, many open-source toolkits are used in the IPS, such as GDAL, OpenLayer, and GeoServer. To better display the information, the IPS adopts the information publishing scheme of integrating the WebGIS and mobile clients, which incorporates the advantages of GIS system, has a low cost and can be updated quickly and easily. (Figures 4 and 5).

III. CASE STUDY The research area is located in the Semiarid Ecosystem

Research Station of the Loess Plateau (36°02′N, 104°25′E, 2400 m above sea level), Lanzhou University. The area has a medium temperate semiarid climate, with an annual mean air temperature of 6.5 ◦C, a maximum of 19.0 ◦C (July) and minimum of −8.0 ◦C (January). The mean annual precipitation is 320mm, about 60% of which is in July–September, and the average annual free water evaporation is about 1300mm. The water table is very deep, so ground water is unavailable for plant growth. The soil is Heima soil (Calcic Kastanozem, FAO Taxonomy) with 22.9% water content field capacity (gravimetric) and 6.2% permanent wilting point [2]. Because of the temperature and water restrictions, historically, wheat is the major crop in the area. In recent years, plasticulture has effectively mitigated water and temperature limitations in this region and greatly increased the crop production. Plasticulture has become one of the most widely used agricultural practices in the area. Although this technique tends to produce greater grain yield and economic benefit compared to conventional cultivation [3], it needs significant inputs of money and labor every year, and results in soil dryness in the deep soil layers. To minimize investment, improve economic benefits and overcome the shortage, the EAM system was used in this area.

Figure 4. Web-based interfaces.

Figure 5. Mobile clients

IV. DISCUSSION Data collection and analysis is the basis of the model and

simulation system. Multi-source data integration and data mining is a hot spot in current computer science. In this study, based on Open Geospatial Consortium (OGC) standard, hydrological, meteorological, agricultural management practice, and other data were integrated into a geospatial database. Meanwhile, the data collection system gives full consideration of the existing weather stations, sensor networks, and existing databases of different agencies. The EAM system not only monitors the condition of the current ecosystem, it also can simulate the human activity and management practices caused nutrient redistribution and cycling. As the process-based biogeochemical model based on a detailed processing description of thermal, water, carbon and nitrogen cycle of ecosystem, it can provide a basis for assessment of the impact of various management practices on an ecosystem. The EAM system adopted the network information distribution system and can quickly, effectively, conveniently and inexpensively release system information. Furthermore, the system can effectively resolve the conflict between data confidentiality and open services by fully guaranteeing the security of the data source. In addition, the network GIS publication system not only can serve the decision-maker, but also promote public understanding and participation in agricultural sustainability and ecosystem vulnerability research.

Supported by the Fundamental Research Funds for the Central Universities

V. CONCLUSION This paper describes an ecosystem assessment and

management system, which integrated sensor networks, process-based model, GIS technology and mobile clients, was used in the Loess Plateau. The system establishment consolidated data fusion, data mining, biogeochemical modeling, WebGIS, Mobile client and wireless network technologies. By integrating the existing meteorological and hydrological monitoring information and using the latest network solutions, the system can collect and process timely information, provide efficient scientific support for eco-management programs and protection of the ecosystem. In the future, our target is to integrate more social and economic information, as well as more real-time sensors into the EAM platform and to adopt a series of social models into the system.

ACKNOWLEDGMENT This research was supported by Fundamental Research Funds for the Central Universities.

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