Elsevier

Environmental Modelling & Software

Volume 83, September 2016, Pages 179-192
Environmental Modelling & Software

A spatial system dynamic model for regional desertification simulation – A case study of Ordos, China

https://doi.org/10.1016/j.envsoft.2016.05.017Get rights and content

Highlights

  • A spatial system dynamic model for desertification simulation was developed.

  • Climate, soil water, population, economy, pasturage and land use were coupled.

  • Ten scenarios were designed to simulate desertification dynamics in Ordos, China.

  • Some suggestions from model were given to local government to combat desertification.

Abstract

A spatial system dynamic (SSD) model for simulating desertification was developed by integrating climate, soil water, population, economy, pasturage, and land use. Net Primary Production (NPP) was selected as an intermediate variable to measure the impacts of driving forces and desertification grades. Ordos in China was selected to test the model. Ten scenarios were designed to simulate desertification dynamics in Ordos from 2011 to 2030. The results showed that the SSD model was reliable, and the overall accuracy of desertification degree was approximately 83%. Climate factors had relatively higher sensitivities than other parameters. Although humid climate and sustainable development strategy would reduce desertified land area and desertification degree before 2025, the rapid increase in pasturage pressure would offset this trend. Arid climate and unsustainable development strategy would accelerate desertification expansion. For local government, it is necessary to persist in the present desertification prevention policy and completely banned grazing after 2025.

Introduction

Desertification is defined as land degradation in arid, semi-arid, and dry sub-humid areas resulting from various factors including climatic variations and human activities (UNCCD, 1994). The interaction of driving forces at different spatio-temporal scales leads to a highly complex desertification process (Prince, 2002, Peters and Havstad, 2006). Rapid urbanization, intensive land use, large-scale ecological projects, and significant climate variation have made desertification dynamics more complicated, especially in the past 30 years (Zhang et al., 2012, D'Odorico et al., 2013, Wang, 2014, Xu et al., 2014). Thus, modeling desertification dynamics as a function of climatic variations and human activities is necessary in order to predict desertification risk, evaluate the impact of climate change, and support policy-making for desertification rehabilitation.

Numerous studies have attempted to predict and simulate desertification dynamics at different temporal-spatial scales (Van Delden et al., 2007, Ibanes et al., 2008, Helldén, 2008, Feng et al., 2010, Rasmy et al., 2010). For example, some studies used regression models, Markov models, or Cellular Automata (CA) models to simulate desertification by considering the statistical relationship between desertification dynamics and driving forces in a certain historical period (Liu et al., 2008, Ding et al., 2009, Feng et al., 2010). Although these models have a robust statistical basis, they are limited in their ability to explain the process of desertification, especially when modeling the impact of social driving factors and supporting policy-making. Furthermore, these models assumed that these relationships will not change in the future. Desertified lands represent open and complex systems. Multi-level feedbacks and non-linear impacts are two important characteristics of the desertification driving process (Peters and Havstad, 2006, Veron et al., 2006). For example, the interactions in the livestock-grass-soil system are extremely important for desertification at small scales (Hahn et al., 2005, Ibanez et al., 2007), but the effect of these interactions will be enhanced or weakened within a large-scale analysis that incorporates climate variation and other human activities. With the development of systems theory, an increasing number of studies are attempting to simulate desertification using the method of System Dynamics (SD). As a classical SD model, the predator-prey model has been frequently used to construct the relationship between humans (predator) and natural resources (prey) in rangeland in order to stimulate desertification dynamics. In 1995, Puigdefábregas developed a desertification model using the predator-prey approach that considered the effect of a closed grazing system and human migration (Puigdefábregas, 1995). Due to its good scalability, the predator-prey model was gradually enlarged, especially the predator component (Puigdefábregas, 1998, Ibanes et al., 2008; Helldén, 2008; Rasmy et al., 2010). For example, Helldén (2008) developed a human-environment two-level coupled predator-prey model to simulate desertification dynamics in Sahelian; in this model, population stock was described as a function of growth rate, death rate, and resource-dependent migration. Rasmy et al. (2010) developed a dynamic simulation model of desertification in Egypt by considering the relationship among people, livestock, and plant cover. With the increasing demand for policy-support in regional and catchment environment management, simulation of desertification has often been included as one of the functions of a more comprehensive system models. Van Delden et al., 2007, Van Delden et al., 2009, Van Delden et al., 2011) designed a policy-support system for river basin management based on a multi-scale dynamic spatial model of socio-economic and physical processes, and this model included the simulation of land degradation and desertification.

Nevertheless, the SD method still faces some limitations when applied to desertification simulation. For example, several previous studies considered the research region as a whole when developing SD models for desertification simulation (Helldén, 2008, Rasmy et al., 2010). These models neglected the spatial heterogeneity of desertified lands as well as regional-scale driving forces. Furthermore, the degree of desertification was not considered in most simulations. Determining the desertification degree would aid in identifying the structural changes of desertified lands, assessing the effect of policies, and producing more detailed spatial planning for desertification control.

In contrast to classic system dynamic models, a spatial system dynamic (SSD) model can construct the linkage of SD variables to Geographical Information System (GIS), including both data association as well as semantic association (Zhang, 2008, Neuwirth et al., 2015). However, few studies have focused on developing an SSD model for desertification simulation at a regional scale that considers the effect of different natural and social factors (Van Delden et al., 2007). Therefore, this study attempts to develop an SSD model for desertification simulation by combining multi-level interactions between natural factors and socio-economic driving forces. We then apply the model to a typical region suffering desertification in China, the city of Ordos, to simulate desertification dynamics from 2011 to 2030. This model will hopefully support the desertification control policies of the local government.

Section snippets

Study area

The city of Ordos is located in the southwest of the Inner Mongolia Autonomous Region of China, with the range of 37° 41′–40° 51′ N and 106° 42′–111° 31′ E. Ordos has an area of approximately 86,752 km2 and had a population of roughly 1,480,000 in 2010 (Fig. 1). Ordos belongs to the arid and semi-arid transition zone, and is subject to a temperate continental climate. The annual rainfall ranges between 170 and 450 mm. The rainfall increases along a gradient from west to east, and the majority

Model framework

In contrast with other ecological and geographical parameters, desertification is only a description of land status, and it cannot be directly measured using conventional tools and units. Thus, it is necessary to select a common indicator that can reflect desertification status and that can be used as an intermediate variable to model the relationship between driving forces and desertification. Additionally, this indicator should be modeled using spatial data. As one of the most important

Model accuracy assessment

According to Table 5, the simulation accuracy of intermediate parameters is very high, especially for the total population. However, the simulation accuracy for total number of sheep and large animals is relatively lower. For the NPP simulation (Fig. 4), the R2 reached a maximum of 0.93, but the RMSE was relatively higher at approximately 28 gC/m2/a. As an indicator of desertification degree, the higher RMSE of NPP impacts the overall accuracy of desertification simulation. However, this RMSE

Discussion

As one of the most important land surface ecosystems, desertified land and its dynamics are very complex due to the interaction of various factors at different temporal and spatial scales. In particular, the high spatial heterogeneity of human activities that can exhibit either positive or negative impacts creates great uncertainty in the reversal and expansion processes of desertification (Prince, 2002, Evans and Geerken, 2004). From this perspective, the SSD model can fulfill the requirements

Conclusion

Desertification simulation is very important. It can be used to predict desertification dynamics in the future, and it can also be employed by policy-makers to evaluate and implement desertification control policies and projects. In this study, an SSD model was developed by incorporating spatial data into a traditional SD model. NPP was selected as a common indicator to measure the impacts and feedbacks of six driving forces and measure the different desertification grades. The application and

Acknowledgements

This research was jointly supported by the National Natural Science Foundation of China (Project 71573245 and 71103170), and the National Key Technologies R&D Program of China (2012BAC19B09).

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