Cities play a leading role in climate change actions and solutions, yet city-level case study sources remain fragmented, biased towards large cities, and inaccessible to local practitioners, notably in the Global South. In response, the Urban Climate Change Research Network (UCCRN) is developing a City Solutions Case Study Atlas (City CSA), a centralized and searchable online platform that integrates diverse case studies focused on climate solutions with an interactive global map containing multiple data layers. The UCCRN City CSA provides an evidence base for academics, urban policymakers, city practitioners, city networks, civil society, and the financial sector to facilitate equitable knowledge transfer and support the development and implementation of context-specific, science-informed urban climate solutions. This paper presents the framework, metadata, and structure of the UCCRN City CSA and assesses metadata search filters and large language model (LLM)-assisted discourse analysis as complementary tools for three user types.

Global climate change science-policy assessments have historically relied on emissions and mitigation scenarios, with relatively limited uptake in adaptation or vulnerability research. Reframing global climate scenarios can enhance their relevance in these fields. A systematic review of 155 studies involving regional scenario development reveals five key perspectives for regionalising the global Shared Socio-economic Pathways (SSP) narratives. While top-down approaches largely dominate, multigenerational regional scenario analyses are emerging, where first-generation regional SSPs provide context for second-generation or further extensions. Participatory methods increasingly integrate bottom-up approaches, offering novel insights into cross-scale consistency. By positioning global scenarios as both boundary objects and boundary conditions, this study highlights the opportunity to expand towards more diverse regional scenarios, potentially broadening engagement with impact, adaptation, and vulnerability scholars beyond the Global North.

Every five to seven years, the Intergovernmental Panel on Climate Change (IPCC) convenes the climate science community to assess the latest knowledge on climate change relevant to policy-makers. This generally takes the form of Assessment Reports (AR) covering the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation. With each cycle, these reports have grown in scope, length, number of referenced papers, and underpinning datasets. During the sixth assessment cycle, a large-scale collective effort went into archiving digital products assessed and generated through the IPCC process. The main objectives driving this initiative are making IPCC’s work more transparent, improving the reproducibility and reusability of the assessment outcomes, better utilization of the services of the IPCC Data Distribution Centre (DDC), and, more generally, compliance with best practices in open science. This paper expands on the motivations for the curation and preservation of digital objects in the IPCC. It gives an overview of how FAIR (Findable, Accessible, Interoperable, Reusable) and open data principles have been implemented in practice and explores some of the successes and setbacks of the AR6 experience. It concludes with recommendations for consolidation and expansion of the approach for AR7. These include a tighter integration of digital curation activities in the IPCC timeline and workflows, better support of IPCC authors and contributors through early training and use of suitable software, improved standardization and harmonization of data and software handling across Working Groups (WGs), and close collaboration with key external data providers and research organizations.

Abstract Particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) increases mortality and morbidity.1,2 PM2.5 is composed of a mixture of chemical components that vary across space and time.3 Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their US-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. Here, we developed the first national super-learned models across the US for hyperlocal estimation of annual mean elemental carbon (EC), ammonium (NH4+), nitrate (NO3-), organic carbon (OC), and sulfate (SO42-) concentrations across 3,535 urban areas at a 50-m spatial resolution, and at a 1-km resolution for non-urban areas from 2000 to 2019. Using ensembles of machine learning models and ~82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. We found remarkable spatiotemporal intra-urban and inter-urban variabilities in PM2.5 components. We anticipate our work to be a critical milestone for conducting new studies on individual and combined health risks of PM2.5 components, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM2.5 composition. Urban planners and regulators may also use these predictions for selecting locations of new daycares, schools, nursing homes, or air-quality monitors.

Abstract Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is a multi-million human silent killer worldwide and contains numerous trace elements (TEs). Understanding TEs relative toxicity is largely limited by lack of data. Here, we used ensembles of machine learning models to generate ~163 billion predictions estimating annual means of ten TEs, namely bromine, calcium, copper, iron, potassium, nickel, lead, silicon, vanadium, and zinc across 3,535 contiguous US urban areas at 50-m spatial resolution and at 1-km in non-urban areas from 2000-2019. Our results highlight substantial intra-urban and inter-urban variations, shrinkages, stagnations, or expansions of hotspots, and trends across 20 years. These data open avenues for future research from epidemiological studies to environmental justice analyses and more.

The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change provides a range of services to support the IPCC Assessment Process. The role of the DDC has evolved considerably since it was established in 1997, responding to the expanding range and complexity of the data products involved in the IPCC assessment process. The role of the IPCC assessments has also evolved from considering whether anthropomorphic climate change might have unwelcome consequences and how those consequences would vary under different socio-economic scenarios to reporting on the likely outcome of different global policy options. The DDC works both with datasets which underpin the key conclusions from the assessment and, increasingly, with data products generated by the scientists engaged in the assessment. Applying FAIR data principles to data products being produced in the highly constrained context of the assessment process brings many challenges. Working with the Technical Support Units of the IPCC Working Groups and the IPCC Task Group, the IPCC DDC has helped to create a process that not only captures information needed to document data products but supports the consistent and clear description of figures and tables within the report.

The Intergovernmental Panel on Climate Change (IPCC) currently prepares its Sixth Assessment Report (AR6). Its authors assess peer-reviewed scientific literature and recent climate datasets to inform policy-makers about the current state of the science regarding climate change and its impacts, as well as adaptation and mitigation options. For AR6, efforts are underway to make its main results FAIR and preserve them in the TRUSTworthy repositories of the IPCC Data Distribution Centre (DDC), jointly managed by CEDA, DKRZ, and CIESIN. The AR6 FAIR initiative was kickstarted by the IPCC DDC and Working Group I (WGI) [Stockhause et al., 2019], then adopted by IPCC TG-Data (Task Group on Data Support for Climate Change Assessments) shortly after its creation. All three WGs have adopted the FAIR data guidelines. IPCC assessments are large and diverse in in terms of scientists involved as well as included scientific objects. Challenges for digital data curation are related to the scale and diversity of papers, reports, datasets, the variety of software, and the different familiarity of the scientists with these technical aspects. The following priority areas for improved data stewardship were selected based on the aims to enhance the traceability of AR6 key findings and their reusability: preserve figure datasets in the DDC; - preserve analysis software; . preserve main input datasets in the DDC; . assemble datasets and provenance information on the figure creation from IPCC authors; and . interlink datasets to the IPCC report. Datasets are transferred to the DDC at the end of AR6. The DDC partners are responsible to preserve the data for future reuse by different stakeholders and under a variety of current and future scientific and policy-related questions. As the role for the DDC expands within the IPCC, new partners are sought. The TRUST principles provide a framework for the communication of DDC tasks to different stakeholders, e.g. to countries interested to host a DDC. The presentation will give an overview over the IPCC AR6 approaches towards FAIR data maintained in TRUSTworthy repositories, their challenges, their approach to meet these challenges and open questions, e.g. the integration of digital data into the IPCC Error Protocol, targeted within TG-Data.

The Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) have provided the scientific basis underpinning far reaching policy decisions.The reports also have a huge influence on public debate about climate change. The IPCC is not responsible either for the evaluation of climate data and related emissions and socioeconomic data and scenarios or for the provision of advice on policy (reports must be “neutral, policy-relevant but not policy-prescriptive”). These omissions may appear unreasonable at first sight, but they are part of the well-tested structure which enables the creation of authoritative reports on the complex and sensitive subject of climate change. The responsibility for evaluation of climate data and related emissions and socioeconomic data and scenarios remains with the global scientific community. The IPCC has the task of undertaking an expert, objective assessment of the state of scientific knowledge as expressed in the scientific literature. The exclusion of responsibility for providing policy advice from the IPCC remit allows the IPCC to stay clear of discussions of political priorities. These distinctions and limitations influence the way in which the findable, accessible, interoperable, and reusable (FAIR) data principles are applied to the work of the IPCC Assessment. There are hundreds of figures in the IPCC Assessment Reports, showing line graphs, global or regional maps, and many other displays of data and information. These figures are put together by the authors using data resources which are described in the scientific literature that is being assessed. The figures are there to illustrate or clarify points raised in the text of the assessment. Increasingly, the figures also provide quantitative information which is of critical importance for many individuals and organisations which are seeking to exploit IPCC knowledge. This presentation will discuss the process of implementing the FAIR data principles within the IPCC assessment process. It will also review both the value of the FAIR principles to the IPCC authors and the IPCC process and the value of the FAIR data products that the process is expected to generate.

The information provided in the Intergovernmental Panel on Climate Change (IPCC; http://ipcc.ch) Assessment Reports (ARs) inform climate change policy development. Within the IPCC the scientific coordination of the ARs is conducted by three Working Groups (WGs) comprising of the Bureaus supported by their Technical Support Units (TSUs). Data management support is provided by the IPCC Data Distribution Centre (DDC; http://ipcc-data.org), which is overseen by the Task Group on Data Support for Climate Change Assessments (TG-Data; formerly TGICA). The DDC is a federated structure that is currently managed by the Centre for Environmental Data Analysis (CEDA; http://www.ceda.ac.uk/), United Kingdom; the World Data Center for Climate (WDCC; http://www.wdc-climate.de), Germany; and the Center for International Earth Science Information Network (CIESIN; http://www.ciesin.columbia.edu/) at Columbia University, U.S. For the IPCC Sixth Assessment cycle (AR6), analyses of climate simulations and observations published in scientific literature will be assessed. The reports will include figures and tables prepared from the underlying digital information. The DDC plays an increasingly important role in facilitating the exchange of data, as well as curating the assessed datasets, scripts and provenance records to facilitate the assessment process and to support the traceability of AR6 results through long-term continuity of data management and curation. These issues, among others, are addressed by the DDC support group (https://cedadev.github.io/ipcc_ddc) currently consisting of members from the three TSUs and the three DDC managers.

Purpose - The purpose of this paper is to investigate the impact of three types of peer monitoring and punishment tools on the performance of a group contract for the control of agricultural non-point source pollution (ANPSP) in China. Design/methodology/approach - Experimental economics. Findings - All the three tools result in efficiency improvement and show little difference in performance. In addition, they break the theoretical Nash equilibrium of the team entry auction and help to better reveal bidders' private cost information. Originality/value - To the authors' knowledge, this study can be the first laboratory experiment study in the area of ANPSP in China and might provide some beneficial lessons for China's policy-makers.

Revealing the temporal and spatial changes on built-up land expansion and population growth is extremely important for city's sustainable development. Although the differences in land and population growth have been examined, the range and the influential factors of such a gap have not been fully studied. In this research the ratio of the land expansion rate to the population growth rate is used as coordination degree to identify the trend of the “land-population” coordination with the case study of the Yangtze River Delta Region, China by means of spatial analysis and regression. Results indicate notable built-up land expansion and demographic change in the research area. The coordination degree increased from a value of 2.28 in Period I (1990–2000) to 3.12 in Period II (2001–2014), further away from the ideal value (i.e. 0.8–1.4). Overall, the coordination level in central region of the study area is better than those of the North and the South. Regression analysis shows that neighborhood and per capita GDP are two significant influential factors of built-up land expansion and population growth in both periods, and that the impact of “neighborhood” has intensified over time. These findings demonstrate that socioeconomic situation of geographically neighboring cities contribute a lot to the coordinated development of the local population and land.

Historical land use information is essential to understanding the impact of anthropogenic modification of land use/cover on the temporal dynamics of environmental and ecological issues. However, due to a lack of spatial explicitness, complete thematic details and the conversion types for historical land use changes, the majority of historical land use reconstructions do not sufficiently meet the requirements for an adequate model. Considering these shortcomings, we explored the possibility of constructing a spatially-explicit modeling framework (HLURM: Historical Land Use Reconstruction Model). Then a three-map comparison method was adopted to validate the projected reconstruction map. The reconstruction suggested that the HLURM model performed well in the spatial reconstruction of various land-use categories, and had a higher figure of merit (48.19%) than models used in other case studies. The largest land use/cover type in the study area was determined to be grassland, followed by arable land and wetland. Using the three-map comparison, we noticed that the major discrepancies in land use changes among the three maps were as a result of inconsistencies in the classification of land-use categories during the study period, rather than as a result of the simulation model.

Vegetation plays an important role in the energy exchange of the land surface, biogeochemical cycles, and hydrological cycles. MODIS (MODerate-resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) is considered as a quantitative indicator for examining dynamic vegetation changes. This paper applied a new method of integrated empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) to detect the vegetation decreasing cover in Jiangsu Province of China. The empirical orthogonal function (EOF) statistical results provide vegetation decreasing/increasing trend as prior information for temporal unmixing analysis. Temporal unmixing analysis (TUA) results could reveal the dominant spatial distribution of decreasing vegetation. The results showed that decreasing vegetation areas in Jiangsu are distributed in the suburbs and newly constructed areas. For validation, the vegetation’s decreasing cover is revealed by linear spectral mixture from Landsat data in three selected cities. Vegetation decreasing areas pixels are also calculated from land use maps in 2000 and 2010. The accuracy of integrated empirical orthogonal function and temporal unmixing analysis method is about 83.14%. This method can be applied to detect vegetation change in large rapidly urbanizing areas.

There is a large amount of population moved from countryside to cities in China during its urbanization in the past two decades. The majority of these people have no formal qualifications for city residency, and they are so-called ‘floating population’. The increase of this group of people has induced the pressure of land use in cities, and the contradiction between the demand and the supply of urban land has been intensifying particularly in those developed regions in China. This paper examines the impacts of floating population on urban land by presenting the interrelations between floating population and urban land expansion from the perspective of production land and living land. Structural equation model (SEM) is employed in conducting the analysis. The result shows that the floating population alone does not have direct effect on urban land expansion, but have indirect impacts through engaging in the production or living process. It is particularly interesting that floating people's living conditions have no direct positive effect on the increase of construction land. Based on the research results, suggestions are offered for improvements in government policy towards a more sustainable and integrated urbanization, including the provision of housing support, the formation of more urbanized society and sustainable development.

Decadal to centennial land use and land cover change has been consistently singled out as a key element and an important driver of global environmental change, playing an essential role in balancing energy use. Understanding long-term human-environment interactions requires historical reconstruction of past land use and land cover changes. Most of the existing historical reconstructions have insufficient spatial and thematic detail and do not consider various land change types. In this context, this paper explored the possibility of using a cellular automata-Markov model in 90 m × 90 m spatial resolution to reconstruct historical land use in the 1930s in Zhenlai County, China. Then the three-map comparison methodology was employed to assess the predictive accuracy of the transition modeling. The model could produce backward projections by analyzing land use changes in recent decades, assuming that the present land use pattern is dynamically dependent on the historical one. The reconstruction results indicated that in the 1930s most of the study area was occupied by grasslands, followed by wetlands and arable land, while other land categories occupied relatively small areas. Analysis of the three-map comparison illustrated that the major differences among the three maps have less to do with the simulation model and more to do with the inconsistencies among the land categories during the study period. Different information provided by topographic maps and remote sensing images must be recognized.

Despite the unprecedented rate of urbanization throughout the world, human society is still facing the challenge of coordinating urban socioeconomic development and ecological conservation. In this article, we integrated socioeconomic data and spatial metrics to investigate the coupling relationship between intensive land use (ILU) system and landscape ecological security (LES) system for urban sustainable development, and to determine how these systems interact with each other. The values of ILU and LES were first calculated according to two evaluation subsystems under the pressure-state-response (PSR) framework. A coupling model was then established to analyze the coupling relationship within these two subsystems. The results showed that the levels of both subsystems were generally increasing, but there were several fluctuation changes in LES. The interaction in each system was time lagged; urban land use/cover change (LUCC) and ecosystem transformation were determined by political business cycles and influenced by specific factors. The coupling relationship underwent a coordinated development mode from 1992–2012. From the findings we concluded that the coupling system maintained a stable condition and underwent evolving threshold values. The integrated ILU and LES system was a coupling system in which subsystems were related to each other and internal elements had mutual effects. Finally, it was suggested that our results provided a multi-level interdisciplinary perspective on linking socioeconomic-ecological systems. The implications for urban sustainable development were also discussed.

Analyzing spatiotemporal changes in land use and land cover could provide basic information for appropriate decision-making and thereby plays an essential role in promoting the sustainable use of land resources, especially in ecologically fragile regions. In this paper, a case study was taken in Zhenlai County, which is a part of the farming-pastoral ecotone of Northern China. This study integrated methods of bitemporal change detection and temporal trajectory analysis to trace the paths of land cover change for every location in the study area from 1954 to 2005, using published land cover data based on topographic and environmental background maps and also remotely sensed images including Landsat MSS (Multispectral Scanner) and TM (Thematic Mapper). Meanwhile, the Lorenz curve and Gini coefficient derived from economic models were also used to study the land use structure changes to gain a better understanding of human impact on this fragile ecosystem. Results of bitemporal change detection showed that the most common land cover transition in the study area was an expansion of arable land at the expense of grassland and wetland. Plenty of grassland was converted to other unused land, indicating serious environmental degradation in Zhenlai County during the past decades. Trajectory analysis of land use and land cover change demonstrated that settlement, arable land, and water bodies were relatively stable in terms of coverage and spatial distribution, while grassland, wetland, and forest land had weak stability. Natural forces were still dominating the environmental processes of the study area, while human-induced changes also played an important role in environmental change. In addition, different types of land use displayed different concentration trends and had large changes during the study period. Arable land was the most decentralized, whereas forest land was the most concentrated. The above results not only revealed notable spatiotemporal features of land use and land cover change in the time series, but also confirmed the applicability and effectiveness of the methodology in our research, which combined bitemporal change detection, temporal trajectory analysis, and a Lorenz curve/Gini coefficient in analyzing spatiotemporal changes in land use and land cover.

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects: (1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. (2) Historical maps and pictures offer visual and spatial quantitative land-cover information. (3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. (4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. (5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with, high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.

Although rural out-migration has significantly transformed land use at the local to regional scale, the links between rural out-migration and land use change are not well understood. This paper connects Zelinsky's mobility transition model to land use transition theory and identifies the impacts of rural out-migration on land use transition. It then explores the significant influences of rural out-migration on land use transition in China. Since the introduction of economic reforms in 1978, China has undergone rapid and significant changes. Extensive rural out-migration has transformed China from a land-attached agricultural society to an urban and industrial society. This has produced several contrasting land use trends: increased land demand in urban areas at the expense of high-quality cultivated land, increased number of total settlement areas and emerging "hollowed villages" in the countryside. China's policies addressing these problems could benefit to other developing countries, such as restricting frontier clearing through land zoning and other ecological protection policies; encouraging nonmigrants to adjust their agricultural land holdings; protecting nonmigrants' interest through subsidizing agricultural land, and improving rural infrastructure and farmers' living conditions. Rural out-migration is thus a critical element in addressing the fundamental question of land use how to balance the land demand for economic development, food security and conservation. This article explores the impacts of rural out-migration on land use change, analyzes the process of migration and land use transition and then examines how rural out-migration affects land use transition in China. This paper also explores future land use change in China, by considering the trend of rural-urban migration and the dynamics of population transition. In so doing, we try to link current rural out-migration dynamics and land use change to facilitate future research and policy considerations. We propose that in order to facilitate policymaking, further research should take a multiscale perspective: cross-country research should be based on an understanding of the dynamics and issues of rural out-migration and land use change in developing countries with different characteristics; country-level research should focus on land use change and problems caused by rural out-migration and its spatial characteristics; and community and household-level research should examine the effects of out-migration of household or household members on agricultural and other land use change. (C) 2013 Elsevier Ltd. All rights reserved.

Karst is a type of landscape which forms under a specific combination of geological conditions, precipitation, and temperature (Fleury 2009). It contains caves and extensive underground water systems that develop from especially soluble rocks such as limestone, marble, and gypsum (Ford and Williams 2007). Karst regions have always been characterized by environmental problems such as an impoverished ecosystem, soil degradation and erosion, deterioration of water quality and landform destruction (Urich 2002). Thus, a karst region can be considered among the most vulnerable land systems in the world. The Karst mountainous region of Southwest China is one of the largest karst continuum belts in the world (Huang et al. 2008). Karst geomorphology is mainly concentrated in the Yunnan, Guizhou, and Guangxi provinces. There, the karst system experienced rocky desertification, a process of land degradation whereby an area formerly covered by vegetation and soil is transformed into a rocky landscape almost completely devoid of soil and vegetation. Rocky desertification always leads to progressive impoverishment of local residents, a situation typically exacerbated by pressures from population growth and improper uses of land (Yuan 1997). At one time researchers called it “cancer of the land” (Zhang et al. 2006). Degradation is most severe in Guizhou province (Zhang et al. 2006; Huang et al. 2008). Biophysical characteristics such as seasonal drought and floods, vegetation degradation, soil erosion, and landslides together with such social-economic issues as poverty and concentrated ethnic minority groups living in remote and less developed areas with low education level, make for a more critical situation than in other provinces (Xing et al. 2009). The total area of Guizhou province is 176 167 km2, of which 92.5 % is hilly and 61.9 % is karst (Xiong 2002). In 2005, 21.3 % of the land was under rocky desertification, while another 31.2 % was in the process of being transformed into rocky desertification land (Fig. 1) (Xiong et al. 2009). There were 23.0 million people, or 66.2 % of the total population, living in the rural areas of Guizhou province in 2010 (Guizhou Bureau of Statistic 2011). On a national level nearly 50.3 % live in rural areas (National Bureau of Statistics 2011). Per capita cultivated land is less than 0.05 ha. In order to make a living in such harsh conditions, local peasants have to cultivate sloping fields, which leads to environmental degradation and further poverty. This vulnerable environmental system co-evolves with a lagging social economy deems this region a hotspot calling for policy attention and research. Interrupting the cycle of poverty is difficult and complex: because of the fragile socio-ecological system, most of the local inhabitants live on subsistence agriculture, it is difficult to extract themselves from poverty trap without external intervention (Reynolds et al. 2007), so assistance from outside such as government, NGOs, and foreign aid is needed. Fig. 1 Rocky desertification rate in Guizhou province (based on data from (Guizhou Bureau of Statistic 2010) Following the great floods in 1998, and in consideration of Guizhou being located at the headwater of Zhujiang and Yangtze river, particular focus was given to the rehabilitation of the ecosystem on the part of national decision-makers (Xiong et al. 2009). From then on, local government and researchers have undertaken a lot of measures to combat further degradation of the fragile environment. Some of the measures succeeded as reported by Zhang et al. (2006). The Chinese central government made a commitment in 2005, to input billions of dollars over the 2008–2015 period to restore the ecosystem in 451 counties of Southwest China experiencing severe rocky desertification. There are 78 counties in Guizhou province, of which 55 were established as pilot projects between 2008 and 2010. A systematic strategy, Integrated Restoration of Small Watershed (IRSW), was devised to apply in these 78 counties between 2011 and 2015. Different from simple measures of ecosystem recovery at site scale (Zhang et al. 2006) and recommendations for antidesertification and researches reported before (Huang et al. 2008), this paper provide a detailed illustration of the systematic approach, Integrated Restoration of Small Watershed (IRSW), for ecosystem restoration in karst areas.