以坡位为空间配置单元的流域管理措施情景优化方法

基于流域过程模型的BMP情景分析是当前流域管理措施评价、非点源污染控制等研究应用中广泛采用的方法,但其通常采用的BMP空间配置单元（地块、农场、水文响应单元或子流域）与坡面上的地形部位关系较弱,难以有效地根据坡面过程特点表达坡面上多种BMP之间的空间配置关系,影响了BMP情景优化效率和结果的合理性。为此,本文提出以坡位单元作为BMP空间配置单元,将各种BMP在不同坡位间合理的空间配置关系显式表达为基于坡位的空间配置规则,通过结合NSGA-II优化算法建立了一套基于坡位单元的BMP空间配置优化方法。应用案例表明,本文构建的基于坡位单元的BMP情景优化方法可有效利用基于坡位的空间配置规则进行BMP情景优化,优化所得的BMP空间配置方案更为合理,优化效率较高。     

中国南方丘陵区非点源污染过程模拟研究进展

随着南方丘陵区开发利用强度的增大,氮、磷非点源污染和水体富营养化问题日益突出。由于流域尺度非点源污染过程的复杂性,难以建立大范围、重复可控的观测和实验环境,模型模拟成为非点源污染治理辅助决策的必要手段。已有学者对南方丘陵区非点源污染过程模拟开展了相关研究,但缺乏对已有成果及其存在问题的系统总结以及对下一步研究方向的分析探讨。本文首先分析了南方丘陵区非点源污染过程的机理,认为该地区的非点源污染过程模拟应满足多过程耦合、空间全分布式及考虑区域特点等要求;然后,对南方丘陵区非点源污染过程模拟研究现状以及存在问题进行了分析,总结了现有研究在描述非点源污染物迁移路径和反映区域特点等方面的不足;最后,从适合南方丘陵区特点的流域空间离散化方法、全分布式非点源污染物迁移路径的构建、特殊地物和人为活动在全分布式非点源污染模型中的综合表达等方面探讨了下一步研究方向,旨在为南方丘陵区非点源污染过程模拟研究提供参考和借鉴。     

简化数字地形分析软件(SimDTA)及其应用——以嫩江流域鹤山农场区的坡位模糊分类为例

为了在精细尺度下定量刻画地形特征,给地理建模提供更准确的定量地形参数,克服当今应用领域中常用商业软件的局限性,本研究讨论了面向栅格DEM的"简化数字地形分析软件"(SimDTA 1.0版本)。SimDTA实现了诸多计算局域和区域地形属性、定量描述地形部位信息的现有算法,以及新建算法。例如,针对一个实际的应用问题--坡位模糊分类,采用以坡位典型位置作为原型的新思路,在SimDTA中,实现了一个新的坡位模糊分类方法,能够克服现有其他方法忽略空间位置信息等问题。这一坡位模糊分类方法和SimDTA中实现的其他功能相结合,形成了一套完整的坡位模糊分类流程。本文通过在东北嫩江流域一个小区的实际应用和讨论来体现SimDTA的有效性和实用性。     

低山丘陵区土壤侵蚀生态工程治理模式及优化配置——以四川省宣汉县拱桥河流域为例

立足于四川省宣汉县拱桥河流域4.3 km2的柏树河村,在小区观测的基础上,提出了5种低山丘陵区土壤侵蚀生态治理模式。根据小流域生态系统的特点,应用多目标优化理论,按不同的治理模式进行了生态工程治理分区,治理措施达到了优化配置,从而使小流域既成为一个水土保持防护体系单元,又成为发展商品生产基地的经济单元,创造人与自然和谐相处的生态环境,为南方丘陵地区小流域提出了一套合理的生态工程治理模式。     

基于多目标遗传算法和FLUS模型的西北农牧交错带土地利用优化配置

西北农牧交错带生态环境脆弱,区位特殊性和生态重要性使其在我国社会经济发展和生态环境保护方面具有重要战略意义。通过对该区域进行土地利用优化配置,使有限的土地资源支撑起生态环境保护和经济发展的重任是本文的出发点。多目标遗传算法和FLUS模型的应用可以从多方面（数量结构、空间布局、综合效益）完善土地利用优化配置,为土地利用优化配置提供更多的选择方案。本文选用多目标遗传算法和FLUS模型对该区域进行2025年的土地利用变化模拟,通过设置自然发展、生态保护优先、经济发展优先、生态-经济均衡4种情景,探讨了如何在兼顾生态环境保护与社会经济发展的情况下进行土地利用的优化配置。结果表明,基于生态-经济均衡情景下的优化方案,土地利用类型的数量结构和空间布局更为合理,其综合效益优于另外3种情景。该情景在合理限制经济发展速度的前提下,使生态建设获得稳定发展,其经济效益较生态保护优先情景下增长了8.96%,生态效益较经济发展优先情景下增长了0.77%,在生态保护与经济发展2种目标之间达到平衡,为西北农牧交错带的土地利用规划提供了决策辅助。     

基于GIS和USLE的东圳库区土壤侵蚀量预测研究

运用GIS、遥感技术与USLE模型相结合预测东圳库区流域土壤侵蚀量,利用GIS平台建立东圳库区基础地理数据库,以10×10m为栅格单元对库区进行离散化,在GIS空间分析功能支持下,结合USLE土壤侵蚀预测模型,根据合适的USLE因子算法生成栅格图层,通过图层运算,预测了东圳库区土壤侵蚀量,进而标识库区土壤侵蚀严重区域,为库区非点源污染控制及水土流失治理提供定量依据,并通过建立实用的泥沙输移比模型结合水文监测数据,对预测结果进行验证。根据USLE方程,采用改进后的SDR方程,预测的渡里流域年输沙量与流域实际监测得到的年输沙量仅相差14.08%,说明本文采用USLE方程模拟流域土壤侵蚀结果可信,可定量评价、拟定水土保持措施方案。     

水资源空间优化配置的群智能算法改进与仿真

本文尝试用群智能算法中的Pareto蚁群算法(PACA)求解复杂的水资源空间优化配置问题。首先,建立了以社会、经济和生态综合效益最大的目标函数,以水质、需水和供水为约束条件的水资源空间优化配置模型,并采用局部信息素强度限制,全局信息素动态更新等策略,对PACA进行改进,使蚂蚁向信息素浓度大的优化边界移动,以提高PACA的全局搜索能力和收敛速度。本文以河南省镇平县为仿真对象,借助RS和GIS,利用改进的PACA求解水资源空间优化配置模型,得到地表水、地下水、外调水的最优配置方案和最佳经济、社会、生态效益方案。通过对PACA性能指标的分析,以及对PACA改进前后解的寻优对比,表明了PACA经过改进后能有效地求解多目标、大规模的水资源空间优化配置模型,提高了寻优性能、收敛速度和全局搜索能力。     

三峡库区小江流域水环境综合区划

本研究以三峡库区小江流域为研究对象,在综合考虑陆域、水体、社会经济对流域水环境影响的基础上,建立了水环境综合区划的指标体系,同时根据DEM划分了流域综合区划的基本单元.在主导因素法和空间叠置法的基础上,通过CIS分析工具,对小江流域的水环境进行了综合区划,分为3个一级区域,分别为北部水源涵养和保育区,南部污染控制区,以...     

基于智慧城市空间大数据的城市信息图谱建构研究

城市大数据信息图谱相较于传统地图信息平台而言,具有可定位、可视化、实时监测的特点;同时,将城市信息图谱平台精确落实到空间地块,则能实现从表层到深层对城市各系统、各单元的全面、综合的信息表达与联动分析。基于此,本文通过大数据采集、人机互动技术及谷地软件等方法,形成从建筑单体-用地地块-道路红线-街区单元-地形地貌的三维建筑精度的城市空间数据库,进而建构出城市多源大数据全信息复合数据库,这一数据库涵盖城市绿化系统、城市市政系统、城市微气候系统、城市产业系统、城市人车系统以及城市意象系统。在此基础上,通过Arc GIS平台将空间形态数据库与复合数据库进行空间耦合,形成基于统一空间坐标系的城市空间大数据信息图谱的基础模型;根据城市规划、城市设计与管理需要进行多对象的大数据组合与相关性分析,获得多源数据融合特征综合信息,进而优化规划和设计的科学决策。     

黄土高原洛河流域地貌发育特征与形成演化

黄土地貌形态特征、发育机理及演化过程等一直是地学研究的热点和前沿。选取黄土高原洛河流域作为研究对象,从坡面尺度、流域尺度、区域尺度分类选取14项地貌因子,较为全面地构建了流域地貌发育形态定量参数体系;提取646个小流域为分析单元,利用主成分分析法,获得了描述流域黄土地貌形态特征的4个主因子;这4个主因子分别反映了4种地貌形态空间格局,即南北渐变型、单中心扩散型、东西过渡型、多中心扩散型。结合流域地貌类型和地貌发育程度特征,认为黄土地貌地形起伏和垂直侵蚀特征反映了研究区的总体地貌空间格局,洛河流域黄土地貌主要是在多种因素影响下,随着水系由下游向上游不断地溯源侵蚀、下切扩展,最终形成了现今形态空间分异格局。     

基于FLUS模型的大都市区土地利用优化模拟——以深圳市为例

采用深圳市2010年、2015年和2016年的土地利用现状数据,运用FLUS模型对自然发展情景、生态安全情景和生态优化情景3种情景下2030年深圳市土地利用结构和空间布局的变化进行模拟。研究结果表明:以2010年为基期模拟2015年土地利用布局的kappa指数为0.862,模拟结果较为理想。3种情景下深圳市2030年土地利用布局既有共性也存在差异。生态优化情景在禁止建设区把部分生产性用地转变为具有重要生态功能的林地,在限制建设区严格控制新增建设用地,在集中连片的控制区内限制生产活动,比自然发展情景和生态安全情景更能达到城市建设和生态保护的双重目标。     

基于CLUE-S模型的辽河流域景观格局空间分布模拟

基于CLUE-S模型,以辽河流域为研究区,利用2000年和2010年两期土地利用数据,采用Logistic逐步回归方程ROC曲线,选择高程、距离、土壤等8种驱动因子,对2010年景观格局进行模拟,并依据2010景观格局、相应的辽河流域土地利用规划,设置3种不同情景,进行2010-2020年景观格局模拟。研究表明：(1)模拟的2010年景观格局,kappa精度达到90%以上,表明CLUE-S模型在辽河流域具有良好的景观格局模拟能力。(2)辽河流域在不同情境下,建设用地均出现不同程度的增加,耕地出现不同程度减少。其中,情景2中,森林有所减少,耕地转化幅度较大,建设用地围绕辽河流域城镇带建设逐步扩展,集中在沈阳、抚顺、鞍山等工业发达城市;情景3中,森林、湿地逐步扩大,表现在东部退耕还林,南部紧靠辽海入口湿地增加。研究结论可为未来辽河流域的生态保护建设及景观格局合理规划提供参考依据。     

GIS COMPONENT BASED 3D LANDSLIDE HAZARD ASSESSMENT SYSTEM：3DSLOPEGIS

In this paper ,based on a new Geographic Information System(GIS) grid-based three-dimensional (3D) deterministic model and taken the slope unit as the study object ,the landslide hazard is mapped by the index of the 3D safety factor ,Compared with the one-dimensional(1D) model of infinite slope,which is now widely used for deterministic model based landslide hazard assessment in GIS,the GIS grid-based 3D model is more acceptable and is more adapt-able for three-dimensional landslide.Assuming the initial slip as the lower part of an ellipsoid ,the 3D critical slip surface in the 3D slper stability analysis is obtained by means of a minimization of the 3D safety factor using the Monte Carlo random simulation.Using a hydraulic model tool for the watershed analysis in GIS,an automatic process has been devel-oped for identifying the slope unit from digital elevation model(DEM)data,Compared with the grid-based landslide hazard mapping method ,the slope unit possesses clear topograhical meaning,so its results are more credible,All the calcula-tions are implemented by a computational program,3DSlopeGIS,in which a GIS component s used for fulfilling the GIS spatial analysis function.and all the data for the 3D slope safety factor calculation are in the from of GIS data (the vector and the grid layers).Because of all these merits of the GIS-based 3D landslide hazard mapping method,the complex algo-rithms and iteration procedures of the 3D problem can also be perfectly implemented.     

Assessment of causes and future deforestation in the mountainous tropical forest of Timor Island,Indonesia

The Mutis-Timau Forest Complex,one of the remaining mountainous tropical forest areas in Timor Island,eastern Indonesia that covers an area of 31,984 ha,tends to decrease gradually.Efforts to secure mountain forest functions and counteract the negative impact of declining forest areas are often constrained by data uncertainty on factors contributing to deforestation.For this reason,this study attempts to develop models of deforestation and predict future deforestation in the Mutis-Timau Forest Complex.We constructed models of deforestation that describe the relationship between deforestation and factors contributing to deforestation using spatial statistical models.In this model,we used the deforestation data for the 1987-2017 period obtained from a previous study as dependent variables and the potential causes of deforestation generated from Geographic Information System spatial analysis as independent variables.Using the probability of deforestation derived from the model,we predicted future deforestation under two different scenarios,namely,business-as-usual(as the reference scenario)and reducing emission fromdeforestation and forest degradation.Our findings showed that a positive relationship exists between probability of deforestation,distance to the settlement,and population density variables,whereas a negative relationship exists between likelihood of deforestation,elevation,slope,distance to the road,distance to the savanna,and forest management unit variables.During the 2017-2030 period,under the business-as-usual scenario,the Mutis-Timau Forest Complex will lose 1327.65 ha in forest area with an annual deforestation rate of 0.54%.Meanwhile,under the reducing emission from deforestation and forest degradation scenario,the overall forest loss was estimated to be 1237.11 ha with an annual deforestation rate of 0.50%.The predicted area of avoided deforestation in 2017-2030 under the reducing emission from deforestation and forest degradation scenario was 90.54 ha.Such data and information are important for the Mutis-Timau Forest Complex authority in prioritizing actions for combating deforestation and designing appropriate forest-related policies and supporting data for reducing emission from deforestation and forest degradation programme or other incentive schemes in reducing deforestation.     

Combining CLUE-S and SWAT models to forecast land use change and non-point source pollution impact at a watershed scale in Liaoning Province,China

Non-point source （NPS） pollution has become a major source of water pollution. A combination of models would provide the necessary direction and approaches designed to control NPS pollution through land use planning. In this study, NPS pollution load was simulated in urban planning, historic trends and ecological protection land use scenarios based on the Conversion of Land Use and its Effect at Small regional extent （CLUE-S） and Soil and Water Assessment Tool （SWAT） models applied to Hunhe-Taizi River Watershed, Liaoning Province, China. Total nitrogen （TN） and total phosphorus （TP） were chosen as NPS pollution indices. The results of models validation showed that CLUE-S and SWAT models were suitable in the study area. NPS pollution mainly came from dry farmland, paddy, rural and urban areas. The spatial distribution of TN and TP exhibited the same trend in 57 sub-catchments. The TN and TP had the highest NPS pollution load in the western and central plains, which concentrated the urban area and farm land. The NPS pollution load would increase in the urban planning and historic trends scenarios, and would be even higher in the urban planning scenario. How- ever, the NPS pollution load decreased in the ecological protection scenario. The differences observed in the three scenarios indicated that land use had a degree of impact on NPS pollution, which showed that scientific and ecologically sound construction could effec- tively reduce the NPS pollution load in a watershed. This study provides a scientific method for conducting NPS pollution research at the watershed scale, a scientific basis for non-point source pollution control, and a reference for related policy making.     

Development of a region-partitioning method for debris flow susceptibility mapping

Debris flow susceptibility mapping(DFSM) has been reported in many studies, however, the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved. In this paper, a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM. First, watershed units were selected as the mapping units and created throughout the entire research area. Four topographical factors, namely, elevation, slope, aspect and relative height difference, were selected as the basis for clustering watershed units. The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts. Then, the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling. Last, a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts. DFSM of Yongji County in Jilin Province, China was selected as a case study, and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method. The area under curve(AUC) values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916. The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM. Hence, the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.     

Placement Optimization Method of FPSO Gas Detectors Based on Leakage Risk

As the first step of the fire/gas-detection systems of floating production storage and offloading(FPSO)units is to iden-tify leakage accidents,gas detectors play an important role in controlling the leakage risk.To improve the leakage scenario detection rate and reduce the cumulative risk value,this paper presents an optimization method of the gas detector placement.The probability density distribution and cumulative probability density distribution of the leakage source variables and environmental variables were calculated based on the Offshore Reliability Data and the statistical data of the relevant leakage variables.A potential leakage sce-nario set was constructed using Latin hypercube sampling.The typical FPSO leakage scenarios were analyzed through computational fluid dynamics(CFD),and the impacts of different parameters on the leakage were addressed.A series of detectors was deployed according to the simulation results.The minimization of the product of effective detection time and gas leakage volume was the risk optimization objective,and the location and number of detectors were taken as decision variables.A greedy extraction heuristic algo-rithm was used to solve the optimization problem.The results show that the optimized placement had a better monitoring effect on the leakage scenario.     

南方山地丘陵土地利用类型的地形影响GIS分析——以江西省为例

分布广泛的山地丘陵,地形复杂多样,生态环境脆弱,不合理的土地利用方式会造成生态环境的破坏,导致严重的水土流失。分析山地丘陵区土地利用的地形控制机制,对于山地丘陵区土地利用开发与水土保持等生态保护之间权衡提供科学依据而具有重要现实意义。因此,本文以南方山地丘陵分布较广泛的江西省为例,在SRTM数字高程模型(DEM)的支持下,利用中国资源环境数据中心基于Landsat遥感解译的2000年土地利用数据,分析了江西省土地利用结构与高程、坡度和坡向等地形因子的关系。结果表明地形因子是影响江西省土地利用方式的一个重要因素,具体表现在:(1)随着高程和坡度的增加,耕地面积及占土地总面积的比例都呈下降趋势,南坡耕地面积和比例都大于北坡的耕地面积和比例;(2)在低海拔区,林地面积占土地总面积比例随着海拔高程的增加而增加,当海拔高于400米时,基本稳定;(3)居民点和工矿用地受高程和坡度的影响较大,受坡向影响较小;(4)草地的面积随着海拔的增加呈下降趋势,但其面积比随着高程增加呈缓慢增加,受坡度和坡向的影响较小。