Geographic approaches to resolving environmental problems in search of the path to sustainability: The case of polluting plant relocation in China

This paper applies a spatial perspective to environmental problems in search for the paths to sustainability, using polluting plant relocation in China as a case study. It examines how environmental improvement in one place may lead to environmental degradation in another place, how geographic concepts such as location, distance, spread and backwash effects, and land use models can help understand such phenomenon, and what the implications are for the environmental Kuznets curve (EKC) and development policies. Field research was conducted from 2006 to 2012 in Chinese cities of Beijing, Dalian, Shanghai, Guangzhou, Wuxi, Hangzhou, and Ningbo. It involved intensive site observations and in-depth interviews with government officials of environmental protection, economic development, and business recruitment, and grassroots environmentalists. The results indicate that environmental improvement in all these cities has led to environmental degradation in their suburbs and rural areas due to relocation of polluting plants. Environmental spread and backwash effects may help explain the severe intra-regional environmental and economic disparities and environmental injustice. The powerful and wealthy may achieve rapid economic growth and environmental recovery at the expenses of the powerless and poor, leading to environmental poverty and sustainability disparities.     

Regional spatial pattern of deep soil water content and its influencing factors

Abstract

Plant root systems can utilize soil water to depths of 10 m or more. Spatial pattern data of deep soil water content (SWC) at the regional scale are scarce due to the labour and time constraints of field measurements. We measured gravimetric deep SWC (DSWC) at depths of 200, 300, 400, 500, 600, 800 and 1000 cm at 382 sites across the Loess Plateau, China. The coefficient of variation was high for soil water content (SWC) in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%). Semivariogram ranges for DSWC at different depths were between 198 and 609 km. Kriged distribution maps indicated that deep soil layers became moister along northwest to southeast transects. Multiple statistical analyses related DSWC to plant characteristics (e.g. plant age explained >21% of the variability), geographical location and altitude (8–13%), soil texture and infiltrability, evaporation zone and eco-hydrological processes (P < 0.05). Regional land management decisions can be based on our DSWC distribution data to determine land uses and plant species appropriate for the soil type and location that would maintain a stable soil water balance. Maintaining infiltrability is of great importance in this and other water-scarce regions of the world.

Editor D. Koutsoyiannis; Associate editor J. Simunek

Citation Wang, Y.Q., Shao, M.A., Liu, Z.P. and Warrington, D.N., 2012. Regional spatial pattern of deep soil water content and its influencing factors. Hydrological Sciences Journal, 57 (2), 265–281.     

Regionalisation of hydrological responses under land-use change and variable data quality

ABSTRACT

Estimating river flows at ungauged sites is generally recognised as an important area of research. In countries or regions with rapid land development and sparse hydrological gauging networks, three particular challenges may arise—data scarcity, data quality, and hydrological non-stationarity. Using data from 44 gauged sub-catchments of the upper Ping catchment in northern Thailand from the period 1995–2006, three relevant flow response indices (runoff coefficient, base flow index and seasonal elasticity of flow) were regionalised by regression against available catchment properties. The runoff coefficient was the most successfully regionalised, followed by base flow index and lastly the seasonal elasticity. The non-stationarity (represented by the differences between two 6-year sub-periods) was significant both in the flow response indices and in land use indices; however relationships between the two sets of indices were weak. The regression equations derived from regionalisation were not helpful in predicting the non-stationarity in the flow indices except somewhat for the runoff coefficient. A partly subjective data quality scoring system was devised, and showed the clear influence of rainfall and flow data quality on regionalisation uncertainty. Recommendations towards improving data support for hydrological regionalisation in Thailand include more relevant soils databases, improved records of abstractions and investment in the gauge network. Widening of the regionalisation beyond the upper Ping and renewed efforts at using remotely sensed rainfall data are other possible ways forward.

EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR T. Wagener     

Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.     

Soil CO2 flux trends with differences in soil moisture among four types of land use in an Ecuadorian páramo landscape

Abstract

Páramo grasslands are important carbon sinks in the Ecuadorian Andes. Although carbon content of páramo Andisols is correlated with high water retention, the effects of differences in soil moisture under different types of land use on soil carbon processes have not been explicitly tested in the Ecuadorian Andes. This study assessed the relationship between soil moisture and soil CO₂ flux among a mature páramo grassland, recently burned páramo grassland, native montane forest, and pine plantation in an Ecuadorian páramo landscape. Soil CO₂ flux was greater in the forest sites compared to the grassland sites. Thus, a shift from grassland to forest cover may have significant implications for soil carbon loss via CO₂ flux. Our results suggest that although soil moisture plays a significant role in differences of soil CO₂ flux rates among land-use types, more investigation into mechanisms for soil carbon loss and how they are driven by land-use change is needed. To our knowledge, these are the first soil CO₂ flux rates reported for the Ecuadorian páramo.     

Historical Land-Use/Land-Cover Changes in a Bottomland Hardwood Forest,Bayou Fountain,Louisiana

Historical surveys have proven important for assessing changes in forest composition and for providing insights into the influences of human activities on forest ecosystems. We used GIS to compare past land-use/land-cover information from General Land Office (GLO) surveys taken in 1849/1851 with current forest composition to illustrate the extensive changes that have occurred in bottomland hardwood forest composition over the past 150 years in East Baton Rouge Parish (EBRP), Louisiana. Agricultural lands, hardwood wetlands, and urban lands increased in area at the expense of rangelands and swamplands. A comparison of forest composition between A.D. 1849/1851 and today illustrates that the bottomlands were once a cypress-tupelo gum association but are now an elm-ash-sugarberry association. These changes are related to expanding urban conditions and encroaching human activities (e.g., levee construction) that substantially changed surface hydrological properties, especially the potential for flooding. Bottomland hardwood forests provide important services to low-lying urban areas mainly through storage of flood waters. Without conservation efforts, bottomland hardwood forests will experience continued human-mediated changes.     

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.     

Factors and mechanism driving the land-use conversion in Jiangxi Province

This article explores the factors and mechanism driving the land-use conversion at regional level by developing and using an econometric approach which is called the simultaneous equations model (SEM). A case study in Jiangxi Province of China is conducted by establishing the SEM, which consists of three equations including agricultural production, land conversion of cultivated land to built-up area and land conversion of cultivated land to forest cover/grassland from 1988 to 2005. And then this paper employs the method of piecewise estimation to represent the influences of the factors such as population, society, economy, location and geophysical conditions on the process of land-use conversion in Jiangxi Province. Estimation results show that population is a predominant factor driving the land-use conversion at counties, while social and economic factors are determinant factors in the short term for the entire Jiangxi Province. Specifically, the size of agricultural population and the magnitude of agricultural input determine the agricultural production to a large extent; population size, plain area proportion at counties and land management policies together affect the direction and magnitude of conversion between cultivated land and built-up area; agricultural population proportion, terrain slope, grain production and non-agricultural industry promote the conversion of cultivated land to forest cover/grassland. Furthermore, the explored mechanism also reveals the underlying causes of the land use changes driven by a series of factors in Jiangxi Province. Finally, this paper concludes that factors proven to play an important role in driving the land-use conversion need to be considered when the land management agencies make out the land use planning to optimize the land use, expand the agricultural production, and conserve the cultivated land.     

沙区聚落模式及人居环境质量评价研究——以宁夏盐池县北部风沙区为例

选取沙化严重但近期沙化防治效果显著的宁夏盐池县北部风沙区为案例,基于SPOT 5遥感影像将沙区聚落按居民点与周边土地的空间关系分成11种类型,并抽象成3个基本模式(放射状、环状、放射+环状)。同时,构建由3大支撑系统的30项指标组成的聚落人居评价指标体系,对11种类型聚落中的典型案例进行人居环境质量评价。结果表明:环状模式聚落的人居环境质量最高,其中又以单环状聚落最优;复杂半放射+半环状聚落次之;放射状聚落相对最差,其中又以居民点与沙地直接接触的有耕地放射状最差。最后,分析了各等级聚落的优势与不足,并结合沙区实际情况提出复杂半放射+半环状聚落中有耕地半放射+半环状的聚落为沙区聚落相对适宜人居的聚落类型。     

基于遥感的无锡市土地利用与过境水质响应关系的研究

以京杭大运河无锡段水质监测数据和2003年7月SPOT-5正射影像为数据源,分析土地利用面积比例与过境水质之间的响应关系。结果表明:以监测断面为中心缓冲区距离的空间变化与水质监测指标之间有明显的响应关系。运河水质污染主要受离河岸较近的土地利用结构的影响。耕地上产生的农业污水,商业和工业中的废水以及大面积裸地上居民生活垃圾和建筑垃圾产生的污水是导致京杭大运河无锡段水质恶化的重要原因。