泥沙输移方程在地貌预测模型研究应用中的局限性

Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfection, (ii) omission of important processes, (iii) lack of knowledge of initial conditions, (iv) sensitivity to initial conditions, (v) unresolved heterogeneity, (vi) occurrence of external forcing, and (vii) inapplicability of the factor of safety concept. Sources of uncertainty that are unimportant or that can be controlled at small scales and over short time scales become important in large-scale applications and over long time scales. Control and repeatability, hallmarks of laboratory-scale experiments, are usually lacking at the large scales characteristic of geomorphology. Heterogeneity is an important concomitant of size, and tends to make large systems unique. Uniqueness implies that prediction cannot be based upon first-principles quantitative modeling alone, but must be a function of system history as well. Periodic data collection, feedback, and model updating are essential where site-specific prediction is required.     

大尺度水循环模拟系统不确定性研究进展

水循环过程受众多自然因素和人为因素影响,决定了水循环系统的变化性和复杂性。水循环系统模型作为研究流域水文循环过程及演变规律的重要工具,必然也存在较大的不确定性,特别是对于大尺度陆-气耦合下的水循环模拟系统,其不确定性来源包括输入和参数不确定性、结构不确定性、方法不确定性以及初始和边界条件不确定性。本文在分析不确定性量化方法和传统水文模型不确定性研究基础上,重点评述当前大尺度水循环系统模拟的不确定性研究进展和存在的瓶颈问题,并介绍一种针对大型复杂动力系统的不确定性量化解决方案和工具系统-PSUADE,基于此讨论PSUADE在大尺度水循环模拟系统不确定性量化过程中的优势。     

土壤风力侵蚀研究现状与进展

土壤风蚀实质上是土壤颗粒在风力作用下发生位移的自然过程,它包含了土壤夹带起沙、空间输移及沉降淀积等三个阶段。风蚀研究的根本任务是对土壤风蚀的范围、强度及数量进行监测、评价以及预测预报。为此,科学家在断面尺度、地块(图斑)尺度以及区域尺度上,以年、月、日、小时等时间尺度展开了研究。当前的风蚀研究主要有以下四个方向:实验室和野外风洞实验研究、野外观测与网络监测、风蚀评价以及风蚀估算与过程模拟研究等。实验室和野外风洞实验有助于人们深入理解风蚀的基本过程;而网络监测数据对于实现风蚀研究从局部到整体的尺度转换具有重要意义;在风蚀评价方面,对风蚀发源地的风蚀评价研究卓有成效,但针对风蚀物运移过程及沉降过程的研究成果还不多见;在风蚀估算和过程模拟方面,一些模型或应用系统已经在不同的区域以不同的时空尺度取得良好的效果,但是要将这些模型和系统在不同的时空尺度上做进一步推广还有许多工作要做。遥感和GIS等现代地理信息技术在区域尺度的风蚀研究中有着显著的优势,并贯穿了风蚀研究的全过程。     

Mass Balance of the Greenland Ice Sheet: Can Modern Observation Methods Reduce the Uncertainty?

The future contribution to sea level change from the large ice sheets in Greenland and Antarctica is composed of two terms: (i) a background trend determined by the past climate and dynamic history of the ice sheets on a range of time scales (decadal, millennial or even longer); and (ii) a rise/fall related to future climate change, whether due to anthropogenic effects or natural climate variability. The accelerating development of remote sensing techniques for monitoring ice sheet behaviour, and the use of high-resolution general circulation models to estimate temperature and precipitation changes are likely to result in improved estimates of the sensitivity of ice sheet mass balance to climate change and thereby to narrow down the uncertainty of contribution (ii). Contribution (i) is much more difficult to assess, because the mass balance displays large temporal variability on year-to-year and even on decadal time scales that masks the long-term trend. So, although modern remote sensing techniques enable accurate measurement of ice sheet surface elevation change, the mass changes derived from such measurements, even if performed over a period of several years, might just reflect a statistical fluctuation around the long-term background trend, which we must know in order to assess the future ice sheet contribution to sea level change on century and longer time scales. The measured volume changes must therefore be evaluated on the background of short- and long-term accumulation rates (e.g. determined from ice cores and high-resolution ice radar) and dynamic model studies of ice sheet evolution on century, millennial and longer time scales. The problems are illustrated by using the Greenland ice sheet as an example.     

SWH双源蒸散模型模拟效果验证及不确定性分析

SWH模型是在经典Shuttleworth-Wallace双源蒸散模型的基础上发展起来的蒸散模型。过去的研究结果表明在站点尺度上SWH模型表现出较高模拟精度,但有关模型对主要参数及驱动变量的敏感性以及模型模拟的不确定性来源等缺乏深入理解与认识。本文通过与51个陆地生态系统站点多年的蒸散观测数据对比,在季尺度、年尺度上验证了全国范围内SWH模型的模拟效果,并分析了关键参数和驱动变量对模型不确定性的贡献大小。结果表明：SWH模型在区域尺度上取得了较好的模拟效果,模拟蒸散与实测值R²均在0.75以上。模型各参数中,冠层导度估算涉及的两个参数对蒸散模拟不确定性影响较大;驱动数据中,归一化植被指数对蒸散模拟不确定性影响较大。尽管部分数据（如降水）因插补存在较大的误差,但总体上气候驱动数据对蒸散模拟的不确定性的贡献仍低于NDVI。     

全球冰冻圈变化预测研究现状

冰冻圈是气候系统重要的圈层,对气候系统有强烈的反馈作用,在全球变暖的背景下,冰冻圈的变化和预测研究愈来愈受到科学界的重视。近年来,冰冻圈的预测研究已经取得重要进展,主要表现在:海冰、积雪冻土等子系统模式发展迅速,开展了不少模式比较计划,这些模式能重现大尺度的季节变化和年际变化特征,模拟能力较以前有了大幅度提高。但模式模拟的不确定性仍普遍存在,主要表现在:冰盖等子系统的模式对于其内部的热力过程、其底部与海洋的相互作用过程缺乏有效的观测手段,认识不够清楚,湖冰河冰模式主要还依赖统计相关模型。随着遥感技术以及资料同化技术的不断应用,各个子系统物理过程认识的不断深入,冰冻圈模式预测将日趋完善,逐渐缩小不确定性。     

Degradation of unconsolidated Quaternary landforms in the western North America

One of the major goals of geomorphology is to understand the rate of landscape evolution and the constraints that erosion sets on the longevity of land surfaces. The latter has also turned out to be vital in modern applications of cosmogenic exposure dating and interpretation of lichenometric data from unconsolidated landforms. Because the effects of landform degradation have not been well documented, disagreements exist among researchers regarding the importance of degradation processes in the dating techniques applied to exposures. Here, we show that all existing qualitative data and quantitative markers of landform degradation collectively suggest considerable lowering of the surface of unconsolidated landforms over the typical life span of Quaternary moraines or fault scarps. Degradation is ubiquitous and considerable even on short time scales of hundreds of years on steeply sloping landforms. These conservative analyses are based entirely on field observations of decreasing slope angles of landforms over the typical range of ages in western North America and widely accepted modeling of landscape degradation. We found that the maximum depth of erosion on fault scarps and moraines is on average 34% of the initial height of the scarp and 25% of the final height of the moraine. Although our observations are limited to fault scarps and moraines, the results apply to any sloping unconsolidated landform in the western North America. These results invalidate the prevailing assumption of no or little surface lowering on sloping unconsolidated landforms over the Quaternary Period and affirm that accurate interpretations of lichen ages and cosmogenically dated boulder ages require keen understanding of the ever-present erosion. In our view, the most important results are twofold: 1) to show with a large data set that degradation affects universally all sloping unconsolidated landforms, and 2) to unambiguously show that even conservative estimates of the total lowering of the surface operate at time and depth scales that significantly interfere with cosmogenic exposure and lichen dating.     

Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km² Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.     

Challenges of calculating dunefield mobility over the 21st century

Attention has been directed towards both the impacts of future climate change on environmental systems and dunefield activity in the past, but there has been relatively little consideration of potential dune mobility in a future and possibly warmer world. This paper considers the use and limitations of four Global Circulation Models (GCMs) (Hadcm3, Hadcm2, CSIRO-mk2b and CGCM1), in combination with simple dune mobility indices to predict the activity of the Kalahari dunefield. It is clear that uncertainties surround GCM resolution and accuracy, mobility index robustness for the calculation of intra-annual dune mobility and data collection for mobility index calibration. Macro-scale studies that look at large areas of the world over long time scales are well suited to GCM and mobility index use, but dune mobility can be variable within a dunefield, and it is the extreme sand transporting events, occurring at high temporal resolutions, that are the most important for short term studies. To investigate intra-annual changes in dune mobility over a specific dunefield techniques such as downscaling, weather generators and probability curve fitting can help provide climate predictions for smaller areas over shorter time frames. However, these methods introduce uncertainty of their own, and they often rely on the accuracy of original GCM predictions or the climate parameter relationships observed at present. Analysis of intra-annual changes also requires mobility indices that can model monthly mobility patterns well, although existing indices have only been used for calculating annual dune mobility potential. When they are used for intra-annual predictions, the lack of lag response between precipitation decreases and the assumed vegetation dieback leads to an exaggerated amplitude pattern of dune mobility throughout the year. Calibration of dune mobility indices to dune mobility observed on the ground is therefore important but is hampered by a lack of observed measurements for individual months. Solutions are available to overcome some of the outlined problems, but they can provide their own set of uncertainties, which combine to further reduce the confidence given to future dune mobility predictions.     

Dynamics of coupled human-landscape systems

A preliminary dynamical analysis of landscapes and humans as hierarchical complex systems suggests that strong coupling between the two that spreads to become regionally or globally pervasive should be focused at multi-year to decadal time scales. At these scales, landscape dynamics is dominated by water, sediment and biological routing mediated by fluvial, oceanic, atmospheric processes and human dynamics is dominated by simplifying, profit-maximizing market forces and political action based on projection of economic effect. Also at these scales, landscapes impact humans through patterns of natural disasters and trends such as sea level rise; humans impact landscapes by the effect of economic activity and changes meant to mitigate natural disasters and longer term trends. Based on this analysis, human-landscape coupled systems can be modeled using heterogeneous agents employing prediction models to determine actions to represent the nonlinear behavior of economic and political systems and rule-based routing algorithms to represent landscape processes. A cellular model for the development of New Orleans illustrates this approach, with routing algorithms for river and hurricane-storm surge determining flood extent, five markets (home, labor, hotel, tourism and port services) connecting seven types of economic agents (home buyers/laborers, home developers, hotel owners/ employers, hotel developers, tourists, port services developer and port services owners/employers), building of levees or a river spillway by political agents and damage to homes, hotels or port services within cells determined by the passage or depth of flood waters. The model reproduces historical aspects of New Orleans economic development and levee construction and the filtering of frequent small-scale floods at the expense of large disasters.     

Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain)

Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.     

中小比例尺风洞模拟理论的研究与应用综述

围绕中小比例尺模型的划分、理论依据、精度范围、粗糙度增强与变形技术,综述近50年来风洞模拟中的理论研究与应用。阐述了各类主要的中小比例尺模型模拟中,基于雷诺数无关的原则,采用传统的表面粗糙度增强技术、高精度数字化地形雕刻技术以及变形比例尺技术,在污染物扩散、风资源评价、风力侵蚀与沉积、森林火风险预报等方面的应用。由于变形比例尺理论的薄弱,研究结果存在许多争议。中小比例尺风洞模拟涉及空间流场的时空概括与转换,未来应加强该领域的深入研究。     

Relationships between morphological and sedimentological parameters in source‐to‐sink systems: a basis for predicting semi‐quantitative characteristics in subsurface systems

The study of source‐to‐sink systems relates long‐term variations in sediment flux to morphogenic evolution of erosional–depositional systems. These variations are caused by an intricate combination of autogenic and allogenic forcing mechanisms that operate on multiple time scales – from individual transport events to large‐scale filling of basins. In order to achieve a better understanding of how these mechanisms influence morphological characteristics on different scales, 29 submodern source‐to‐sink systems have been investigated. The study is based on measurements of morphological parameters from catchments, shelves and slopes derived from a ∼1 km global digital elevation model dataset, in combination with data on basin floor fans, sediment supply, water discharge and deposition rates derived from published literature. By comparing various morphological and sedimentological parameters within and between individual systems, a number of relationships governing system evolution and behaviour are identified. The results suggest that the amount of low‐gradient floodplain area and river channel gradient are good indicators for catchment storage potential. Catchment area and river channel length is also related to shelf area and shelf width, respectively. Similarly to the floodplain area, these parameters are important for long‐term storage of sediment on the shelf platform. Additionally, the basin floor fan area is correlative to the long‐term deposition rate and the slope length. The slope length thus proves to be a useful parameter linking proximal and distal segments in source‐to‐sink systems. The relationships observed in this study provide insight into segment scale development of source‐to‐sink systems, and an understanding of these relationships in modern systems may result in improved knowledge on internal and external development of source‐to‐sink systems over geological time scales. They also allow for the development of a set of semi‐quantitative guidelines that can be used to predict similar relationships in other systems where data from individual system segments are missing or lacking.     

Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods*

Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over‐ and underpredicted observations based on local factors.     

Investigating the system dynamics technique for the modeling and simulation of the development of spatial data infrastructures

Spatial data infrastructure (SDI) is a complex system for which huge investments are being made worldwide. These large-scale investments in the development of SDIs incontrovertibly require reliable design and planning that guarantee a successful outcome. One approach to deal with such an expectation is to model the development process of the SDI system over time. If the model can be translated into the computer-based environment to be used as a virtual world, then the real situation can also be simulated. Such a simulation will enable the SDI coordinators/managers to gain knowledge about the behavior of the system under different decisions and situations and eventually help them to better develop the SDI through the informed decision making. However, a limited number of tools and techniques are currently available in the SDI modeling history in terms of the modeling and simulation of such a complex system. The system dynamics technique based on systems theory is a method for modeling and managing the feedback systems that are complex, dynamic and nonlinear over time. This article addresses the applicability of the system dynamics technique for modeling and simulating the development process of SDIs. It is argued that the system dynamics technique is capable of modeling the interactions among the factors affecting the SDI, the feedback loops and the delays. It is also highlighted that an SDI model based on the system dynamics technique enables the SDI coordinators/managers to simulate the effect of different factors or decisions on various aspects of SDI and evaluate alternative decisions and/or policies prior to making any commitment.     

基于客源地的聚类-ARIMA模型的短期旅游需求预测--以天津欢乐谷主题公园为例

大多数旅游需求预测研究是基于目的地游客总数或消费总量开展的,尚未按不同的旅游目的或客源地细分进行预测.以天津欢乐谷主题公园为案例地,选择2014年第40周到2015年第26周为研究时段,利用通信大数据,提出了一种面向客源地的聚类-ARIMA组合预测模型.通过对不同客源地的时序数据进行聚类,选取各类别中的代表性客源地分别构建ARIMA预测模型.结果表明:对欢乐谷主题公园各客源地分别建模与聚类后通过6个代表客源地建模得到的结果一致;后者可以降低80%的预测成本.该方法具有较高的预测精度和较低的计算成本,适合面向客源地的短期旅游需求预测,可为旅游目的地提供更具针对性的旅游需求管理、分析与决策支撑.     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.     

山区地形开阔度的分布式模型

 地形开阔度是影响山地辐射平衡及其分量的重要地形因子,是山区散射辐射、地形反射辐射等计算的重要参数。在复杂的地形条件下,地形开阔度的计算很难用数学公式描述。 利用数字高程模型（DEM）,全面考虑了坡地自身遮蔽和周围地形相互遮蔽的影响,提出了山区地形开阔度的分布式模型和算法。以1 km×1 km分辨率的DEM数据作为地形的综合反映,计算了起伏地形下中国地形开阔度的空间分布。同时,利用100 m和1 km两个分辨率的DEM数据,从不同DEM分辨率和不同地貌类型两个方面探讨了地形开阔度的空间尺度效应,阐明了区域地形开阔度随地形地貌和空间分辨率的变化规律。所提供的山地开阔度的数据可作为基础地理数据供相关研究应用。     

Mapping ecosystem services at the regional scale: the validity of an upscaling approach

Mapping ecosystem services (ES) over large scales is important for environmental monitoring but is often prohibitively expensive and difficult. We test a hybrid, low-cost method of mapping ES indicators over large scales in Pará State, Brazil. Four ES indicators (vegetation carbon stocks, biodiversity index, soil chemical quality index and rates of water infiltration into soil) were measured in the field and then summarized spatially for regional land-cover classes derived from satellite imagery. The regionally mapped ES values correlated strongly with independent and local measures of ES. For example, regional estimates of the vegetation carbon stocks are strongly correlated with actual measures derived from field samples and validation data (significant anova test – p-value = 4.51^e?9) and differed on average by only 20 Mg/ha from the field data. Our spatially-nested approach provides reliable and accurate maps of ES at both local and regional scales. Local maps account for the specificities of an area while regional maps provide an accurate generalization of an ES’ state. Such up-scaling methods infuse large-scale ES maps with localized data and enable the estimation of uncertainty of at regional scales. Our approach is first step towards the spatial characterization of ES at large and potentially global scales.     

渔子溪流域水文过程影响因素的特征时间尺度分析

采用渔子溪流域三期遥感影像(1986、1994和2002年)获取该区域的土地利用/覆被资料,并结合实际调查数据、采用斑块驱动模型模拟得到该流域1986~2004年间逐年土地利用变化数据;然后根据该模拟数据以及渔子溪流域不同时间尺度的气象和径流资料,采用逐步线性回归和多元非线性回归方法,对渔子溪流域不同时间尺度下降雨-土地利用-径流系统及其内部各元素之间的关系进行了统计分析,结果表明:(1)不同时间尺度下径流量与降雨量的相关关系具有明显差别,土地利用变化对降雨-径流关系有重要影响,在年尺度上,降雨量对径流量的影响要大于气温对径流的影响,而在月尺度上则相反。(2)不同时间尺度下非线性回归的模拟误差要比线性模拟误差下降1.18%~23.8%,且不同影响因子在不同时间尺度上的非线性程度各不相同。