Analysis and identification of key drivers of karst rocky desertification （KRD）can contribute to effective management and restoration. Ignoring heterogeneity leads to statistical bias and influences the specificity of desertification control planning. Taking Guizhou and Guangxi Karst Mountainous Regions as the study area,this paper chose twelve factors,including socioeconomic,spatial distance,topography,climate,soil,lithology and land use,as drivers of KRD. Geographically Weighted Regression modeling （GWR）and embedding spatial factors to the traditional ordinary linear regression （OLS）model were used to analyze the spatial distribution of influence on KRD. An obvious spatial agglomeration of KRD in the study area according to Moran’s I at the significant level of 99% was found. Coefficients of determination （R2）of GWR were much higher for OLS （0.508 vs. 0.156）,indicating a much better fit for the GWR model. Coefficients of GWR between twelve drivers and an obvious spatial distribution of value magnitudes,negative or positive effects and combined types. The specifics of the karst background create a fragile and vulnerable environment that is susceptible to human activities. Meanwhile,intense human activities lead to a sharp change in KRD status,which included the predatory sabotage for casing the severe KRD and the KRD restoration projects for reversing KRD to no KRD. The regression coefficients of the twelve drivers and their linear combined characteristics showed different spatial distributions based on GWR modeling. Using the GWR model revealed the spatial discrimination of the effects of driving forces on KRD and identified key drivers of KRD at the local area,revealing the spatial distribution of the joint effect of different driving forces helping provide a scientific reference to differential KRD control at small watershed scales.

分析和识别石漠化的关键影响因子,有助于有效治理和恢复石漠化。以往研究对影响因子的空间局部差异关注较少,本文以黔桂喀斯特山地为研究区,选取自然及社会经济等12个影响因子,利用地理加权回归（GWR）模型,在普通线性回归的基础上嵌入空间因素,分析石漠化影响因子的空间分异。结果表明:①黔桂喀斯特山地的石漠化Moran’s I大于正态函数在99%显著水平,存在明显空间聚集现象;②GWR模型R²（0.508）明显高于传统统计模型的R²（0.156）,回归模型拟合效果显著提高;③12个影响因子与石漠化关系呈现不同数值大小、正负效应和线性组合关系的空间分布差异;④人类活动叠加在喀斯特特殊的岩性、土壤和植被组合上,显著影响石漠化分布;同时,局部区域高强度人类活动导致石漠化的急剧变化。GWR模型可揭示石漠化影响因子的空间分异规律和局部的关键影响因子,刻画多因子组合作用对石漠化的影响,有助于差别化的小流域石漠化治理。

The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multifactor synthesis are still insufficient. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different morphological types of geomorphology in a typical karst basin based on the RUSLE model and the geographical detector method. The influencing factors, such as land use type, slope, rainfall, elevation, lithology and vegetation cover, have been taken into consideration. Results show that the strength of association between the six influencing factors and soil erosion was notably different in various morphological types of geomorphology. Land use type and slope were the dominant factors of soil erosion in the Sancha River Basin, especially for land use type whose power of determinant (q value) for soil erosion was much higher than that of other factors. The q value of slope declined with the increase of relief in mountainous areas, namely it was ranked as follows: middle elevation hill > small relief mountain > middle relief mountain. Multi-factor interactions were proven to significantly strengthen soil erosion, particularly for the combination of land use type with slope, which can explain 70% of soil erosion distribution. It can be found that soil erosion in the same land use type with different slopes (such as dry land with a slope of 5°and dry land with slopes above 25°) or in the diverse land use types with the same slopes (such as dry land with a slope of 5° and forest with a slope of 5°), varied greatly. This indicates that prohibiting steep slope cultivation and the Grain for Green Project are reasonable measures to harness soil erosion in karst areas. Based on statistics of soil erosion difference between diverse stratifications of each influencing factor, results of risk detector suggest that the amount of stratification combinations with significant difference accounted for 55% at least in small and middle relief mountains. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different morphological types of geomorphology should be investigated to control karst soil loss more effectively.

土壤侵蚀形成机制与影响因素识别是当前研究的核心与前沿议题,然而从多因素综合作用的角度进行定量归因仍需加强。以喀斯特典型峰丛洼地流域为研究区,基于GIS手段和RUSLE模型模拟土壤侵蚀,综合土地利用、坡度、降雨、高程、岩性、植被覆盖度等影响因子,应用地理探测器方法针对喀斯特不同地貌形态类型区进行土壤侵蚀的定量归因研究。结果表明,各影响因子对土壤侵蚀的解释力及因子间耦合作用程度在不同地貌形态类型区差异显著,其中土地利用和坡度是决定土壤侵蚀空间异质的主导因子,但在山地丘陵区,随着地形起伏度的升高,坡度的控制作用下降,即地理探测器q值表现为中海拔丘陵>小起伏中山>中起伏中山;生态探测器显示土地利用对土壤侵蚀的影响相比于其他因子有显著差异;双因子交互作用有助于增强对土壤侵蚀的解释力,土地利用与坡度的协同作用对土壤侵蚀的解释力达到70%以上;对于土壤侵蚀空间分布的差异性检验,风险探测器显示在小起伏中山、中起伏中山等地貌形态类型中,具有显著差异的影响因子分层组合数占比至少55%。因而,喀斯特地区土壤侵蚀的治理应综合考虑不同地貌形态类型区土壤侵蚀影响机制的空间异质性。

Mountainous areas in China face complex problems due to conflicts between natural eco-environmental protection and socio-economic development. Mountain water resources have evolved in the context of climate change and human activities, which further affects their coordination and matching with regional climate resources, land resources and socio-economy. Hence, it is of fundamental significance for mountain development to study these issues to identify key weakness and provide solutions. The paper focuses on three typical mountainous areas under different natural geographic conditions and at socio-economic development levels in China, namely, Taihang Mountains Area (TMA), Hengduan Mountains Region (HMR) and Guizhou-Guangxi Karst Area (GKA), and studies the spatio-temporal variations of precipitation and runoff from 1956 to 2015 by Mann-Kendall test based on national water resources assessment results and statistical data of land use and socio-economy. The characteristics of natural and socio-economic factors are analyzed by matching distance and imbalance index. In this process, four groups of parameters are selected, including water (precipitation and runoff), heat (accumulated temperature and radiation), land (total area and farmland area) and socio-economy (population and GDP). The results indicate that, the water resources endowment of TMA is the worst, and significant reduction of runoff since 2000 makes it even worse, while the precipitation and runoff of HMR and GKA are abundant and there is no obvious change trend. According to the analysis of regional water-heat-human-land matching characteristics, TMA mainly suffers from water shortage, land and heat resources have great disadvantages in most parts of HMR with uneven distribution of water and heat, and land resources are relatively deficient in all counties of GKA. In general, mismatching degree of water and other factors is the highest, and the allocation and coordination of water are the key issue for sustainable development of mountainous areas, especially in TMA.

山地是中国自然生态保护与经济社会发展问题交织、矛盾突出的区域。山地水资源受气候变化和人类活动综合影响发生演变,进一步影响到其与区域气候资源、土地资源以及经济社会之间的协调匹配,研究这些问题对辨识山地发展关键短板、提供山地问题解决思路具有基础意义。根据自然环境特点和经济社会发展状况,选择太行山、横断山和黔桂喀斯特三大典型山地开展研究,基于全国水资源调查评价成果及土地利用、经济社会数据,采用Mann-Kendall检验等方法分析1956-2015年山地降水和径流时空演变特点;采用匹配距离和不平衡指数对山地的水（降水和径流）、热（积温和辐射）、地（面积和耕地）以及社会经济（人口和GDP）等4类要素匹配性进行综合对比分析。结果表明：三大典型山地中,太行山水资源禀赋最差,且2000年以来径流显著衰减,而横断山和黔桂喀斯特降水、径流丰富,且无明显变化趋势;从“水—热—人—地”对比来看,太行山以缺水问题为主,横断山多数地区土地与热资源偏少且水热空间差异大,黔桂喀斯特整体来说土地资源偏少;综合来看,水与其他要素的匹配性最差,特别是在太行山区,水的时空分布及其均衡匹配是保障山地可持续发展的关键。

地貌分类体系是地貌图研制的关键之一,本文在总结国内外地貌及分类研究的基础上,借鉴20世纪80年代的中国1∶100万地貌图制图规范,基于遥感影像、数字高程模型和计算机自动制图等技术条件,归纳总结了数字地貌分类过程中应遵循的几大原则,分析了它们之间的相互关系,讨论了数字地貌分类的各种指标:包括形态、成因、物质组成和年龄等,提出了中国陆地1∶100万数字地貌三等六级七层的数值分类方法,扩展了以多边形图斑反映形态成因类型,以点、线、面图斑共同反映形态结构类型的数字地貌数据组织方式,并详细划分了各成因类型的不同层次、不同级别的地貌类型。中国1∶100万数字地貌分类体系的研究,为遥感等多源数据的陆地地貌解析和制图提供了规范,也为《中华人民共和国地貌图集》的编制奠定了基础,同时为全国大、中比例尺地貌图的分类和编制研究提供了借鉴。

降雨侵蚀力反映气候因素-降雨对土壤侵蚀的潜在作用能力，由于次降雨资料难以获得，一般利用气象站整编降雨资料评估计算降雨侵蚀力的结果进行对比分析，结果表明以日雨量计算多年平均侵蚀力的精度最高，而在4种采用月或年雨量的模型中尽管以逐年月雨量模型表现相对最好，但这4种模型之间差别不明显。同时在降雨量较丰富地区，各类型雨量资料估算侵蚀力的精度也相对较高。

Traditional classic soil erosion models are not applicable in karst areas. In this study, the effective rainfall erosion threshold was obtained and the rainfall erosivity was re-estimated, according to the lithologic difference of the karst critical zone. The soil formation rate was calculated and used as the soil loss tolerance, based on the chemical composition variation of carbonate rocks. The spatial distribution information of karst depression was extracted by geomorphologic-hydrologic analysis, and the soil erosion algorithm in the karst critical zone was modified and improved. The results showed that: (1) The traditional algorithm ignored the specificity of the underlying surface of the karst critical zone. The average rainfall erosivity was estimated to be 47.35%, and the average rainfall erosivity in the karst area is only 59.91% of the non-karst area. (2) The soilless or less soil areas could be miscalculated as the high erosion region by using the conventional algorithm. However, the soil loss tolerance by continuous carbonate, carbonate clastic, carbonate and clastic interbeds were about 0.21, 1.2 and 2.89 t ha-1 yr-1, respectively. (3) Sloping lands and croplands in the depressions are generally regarded as the high incidence area of soil erosion, but the depression of the karst should be the sediment area of surface erosion. The spatial distribution of karst depression was basically coincided with that of carbonate rocks. (4) The traditional algorithm overestimated the soil erosion area at about 27.79%, while the soil erosion amount at approximately 47.72%. Generally, the traditional classic model could greatly overestimate the soil erosion amount in the karst area, therefore, an accurate and applicable model should be established. In addition, due to the slow soil formation rate, the thin soil layer and the less total amount of soil in karst area, the soil loss tolerance was far lower than the erosion standard of non-karst area. The classifying and grading standard and risk assessment method of soil erosion applicable to karst area should be set and established.

喀斯特地区下垫面的特殊复杂性使得地表发生显著坡面径流需要达到更大的降水累积阈值,碳酸盐岩岩石成土速率的缓慢和空间异质性导致喀斯特地区的土壤存量被严重高估和均一化等,因此,传统经典的土壤侵蚀模型在喀斯特地区难以适用。本文依据喀斯特关键带岩性的差异,确定其有效降水侵蚀阈值,并对降雨侵蚀力进行重新测算;根据碳酸盐岩化学成分的差异,计算其成土速率并作为土壤允许流失量;通过地貌—水文分析法提取喀斯特洼地空间分布信息,对喀斯特关键带土壤侵蚀算法进行多次改进和创新。结果显示：① 传统算法忽视了喀斯特关键带下垫面的特殊性,致使其平均降雨侵蚀力被高估47.35%,且喀斯特区域的平均降雨侵蚀力仅相当于非喀斯特区域的59.91%;② 传统算法可能将一些无土或少土可流区计算为土壤的高侵蚀量区,而连续性碳酸盐岩、碳酸盐岩夹碎屑岩、碳酸盐岩与碎屑岩互层的土壤允许流失量仅分别为0.21 t ha^-1 yr^-1、1.2 t ha^-1 yr^-1、2.89 t ha^-1 yr^-1;③ 传统算法通常将有坡度和耕作的洼地视为土壤侵蚀的高发区,但其实际应是地表侵蚀的沉积区,喀斯特洼地在空间上的连续性与碳酸盐岩的分布区基本重合;④ 传统算法高估土壤侵蚀面积27.79%,土壤侵蚀量47.72%。总之,传统经典模型会大大高估喀斯特地区的土壤侵蚀量,因此,应该建立一种精确适用的模型。另外,由于喀斯特地区的成土速率慢而土层薄、总量少,土壤允许流失量远低于非喀斯特区域侵蚀标准,应制定适用于喀斯特地区的土壤侵蚀分类分级标准和风险评价方法。

Spatial stratified heterogeneity is the spatial expression of natural and socio-economic process, which is an important approach for human to recognize nature since Aristotle. Geodetector is a new statistical method to detect spatial stratified heterogeneity and reveal the driving factors behind it. This method with no linear hypothesis has elegant form and definite physical meaning. Here is the basic idea behind Geodetector: assuming that the study area is divided into several subareas. The study area is characterized by spatial stratified heterogeneity if the sum of the variance of subareas is less than the regional total variance; and if the spatial distribution of the two variables tends to be consistent, there is statistical correlation between them. Q-statistic in Geodetector has already been applied in many fields of natural and social sciences which can be used to measure spatial stratified heterogeneity, detect explanatory factors and analyze the interactive relationship between variables. In this paper, the authors will illustrate the principle of Geodetector and summarize the characteristics and applications in order to facilitate the using of Geodetector and help readers to recognize, mine and utilize spatial stratified heterogeneity.

空间分异是自然和社会经济过程的空间表现,也是自亚里士多德以来人类认识自然的重要途径。地理探测器是探测空间分异性,以及揭示其背后驱动因子的一种新的统计学方法,此方法无线性假设,具有优雅的形式和明确的物理含义。基本思想是：假设研究区分为若干子区域,如果子区域的方差之和小于区域总方差,则存在空间分异性;如果两变量的空间分布趋于一致,则两者存在统计关联性。地理探测器q统计量,可用以度量空间分异性、探测解释因子、分析变量之间交互关系,已经在自然和社会科学多领域应用。本文阐述地理探测器的原理,并对其特点及应用进行了归纳总结,以利于读者方便灵活地使用地理探测器来认识、挖掘和利用空间分异性。

Very little information on water and soil loss in karst areas is available. Moreover, traditional methods of assessing soil loss are difficult to apply to this area. In this paper the authors analyze the features of water and soil loss based on location monitoring during the five years (2006-2010), in demonstration areas for combating karst rocky desertification (KRD). The following conclusions are obtained. (1) Soil erosion showed a decreasing trend after a series of comprehensive treatments in the three study regions, and erosion was macroscopically mitigated in terms of landform types. (2) The slope cropland is distributed extensively in Yachi mountain region and erosion is thus serious. With better natural conditions and ecological economy development, erosion in Hongfenghu basin is relatively weak. Due to the severely poor soil cover, the amount of soil erosion is extremely low in Huajiang gorge. (3) Ecologic rehabilitation is the key to combat KRD. Closing hillsides to facilitate afforestation and water conservation are effective measures for the mitigation of soil and water loss. Grassland and economic forests bring both economic and ecological benefits. (4) KRD degrees differ greatly in slope gradient and soil layer. Further classification for soil erosion in karst areas should be proposed based on KRD degrees. (5) Hazard evaluation of subterranean soil loss is one of the tough tasks at present. However, control of key parts such as sinkholes is the efficient way to prevent subterranean loss.

The pattern, process and their relationships are fundamental issues in geography. Patterns and processes in nature, such as ecosystem distribution, regional climatic changes, land use and land cover, and rainfall or runoff series, display complex behavior and often intertwine with scale problems. Many scale-dependent phenomena have been exemplified in scientific researches, and scale issues are found at the center of methodological discussions in both physical and human geography. However, some existed research works failed to make a distinction among the concepts of scale and scaling, to employ consistent scaling paradigm, and to adopt an objective criterion for assessing the scaling effects in geography. This paper attempts to calibrate those deviations and to present new branch subject of geography-Scaling Methodology of Geography. After introducing scaling approaches, upscaling and downscaling, in geography, we place a focus on the research objects, categories and contents of proposed subjects, and three basic principles, scientific principle, economic principle and operational principle, are proposed in scaling works. Finally, ten key issues are presented for establishing the scaling methodology of geography. The key questions are as follows: (1) how does spatial heterogeneity scale in scaling ? (2) how do ratio variables change with scale in scaling? (3) how do dominating processes change with scale in scaling? (4) how does property of processes change with scale in scaling? (5) how does sensitivity change with scale in scaling? (6) how does predictability change with scale in scaling? (7) what is sufficiency for simple aggregation and disaggregation in scaling? (8) how do disturbance factors change with scale in scaling? (9) can scaling transcend several scales and scale fields? (10) is noise factor changeable with scale in scaling?

Digital elevation model (DEM) and terrain analysis based on DEM is scale-dependant. With the view of geoscience analysis and modeling, the paper mainly discusses scale and scale effect of terrain analysis based on grid DEM. Firstly, the paper outlines five common uses of the term "scale" within DEM and terrain analysis which are geographical scale, sampling scale, DEM structure scale, analysis scale and cartographic scale. The geographical scale means the spatial extent of the DEM or the area of coverage; the sampling scale refers to sampling intervals in DEM data collection; the DEM structure scale refers to the resolution of DEM which includes DEM horizontal resolution and vertical resolution; the analysis scale refers to the analysis extent of local windows including window size and threshold value; and the cartographic scale which is used for map reflects the ration between the measurements on a map and the actual measurement on the ground. Secondly, the paper's focus is put on the scale effect of terrain analysis based on DEM. In this section, the uncertainty of DEM and terrain analysis caused by the above scales is classified into single scale effect, cross scale effect and boundary effect. One area of terrain analysis that has been largely ignored is the effect of variability of terrain within the bounds of single grid cell that is referred to the heterogeneity effect of DEM grid size in the paper.We pointed out those and discussed the effect on terrain analysis. At the end of this section, the scale threshold which can be used to indicate the effective extend of terrain attributes is discussed. It is known that the existing DEM with fixed resolution does not always meet the requirement of application. This means that one can deduct more scale terrain models from a fixed resolution DEM. In fact, scaling model can realize the terrain information allocation and aggregation in terrain analysis based on DEM. In this paper, three scaling models including scale deduce model, scale inversion model and multi-scale terrain analysis model are discussed.