基于不同DEM数据源的数字河网提取对比分析——以韩江流域为例

基于HYDRO1K、SRTM3和ASTER GDEM三种DEM数据,利用BTOPMC地形子模型提取韩江流域河网,并作对比分析。结果表明:①SRTM3提取的河网精度最高,HYDRO1K相对最低。②DEM的垂直精度对提取的河网精度起控制作用。ASTER GDEM的水平分辨率较高,但垂直精度不如SRTM3,因而提取的河网精度不如SRTM3。③HYDRO1K提取大尺度流域河网具有一定的精度,但在地势平坦区域的效果较差,HY-DRO1K不宜用来提取小尺度流域河网。④由DEM提取的数字河网精度与当地的地面坡度以及处理DEM的填洼算法有关。     

A study on DEM-derived primary topographic attributes for hydrologic applications: Sensitivity to elevation data resolution

Primary topographic attributes play a critical role in determining watershed hydrologic characteristics for water resources modeling with raster-based digital elevation models (DEM). The effects of DEM resolution on a set of important topographic derivatives are examined in this study, including slope, upslope contributing area, flow length and watershed area. The focus of the study is on how sensitive each of the attributes is to the resolution uncertainty by considering the effects of overall terrain gradient and bias from resampling. Two case study watersheds of different gradient patterns are used with their 10 m USGS DEMs. A series of DEMs up to 200 m grid size are produced from the base DEMs using three commonly used resampling methods. All the terrain variables tested vary with the grid size change. It is found that slope angles decrease and contributing area values increase constantly as DEMs are aggregated progressively to coarser resolutions. No systematic trend is observed for corresponding changes of flow path and watershed area. The analysis also suggests that gradient profile of the watershed presents an important factor for the examined sensitivities to DEM resolution.     

基于光学立体和InSAR的Grove山地区DEM建立和分析

本文分别利用光学立体和In SAR技术生成了东南极Grove山地区15 m分辨率的ASTER DEM和20 m分辨率的In SAR DEM。在利用ASTER立体像对生成DEM的过程中引入ICESat测高数据作为高程控制以减少错误匹配,提高DEM垂直精度;而在利用ERS tandem数据生成DEM后,选取ICESat测高数据对In SAR DEM进行倾斜面纠正,以消除基线不精确估计等带来的影响。通过与未作控制的ICESat测高数据进行比较,评价了两种DEM的精度并对误差进行了分析。同时,比较了两种DEM的差异,并分析了造成这些差异的原因,探讨了两种技术生成南极冰盖DEM的优势和不足。最后结合两DEM的优势,融合生成了Grove山地区高精度的DEM。     

Pre-processing algorithms and landslide modelling on remotely sensed DEMs

Terrain analysis applications using remotely sensed Digital Elevation Models (DEMs), nowadays easily available, permit to quantify several river basin morphologic and hydrologic properties (e.g. slope, aspect, curvature, flow path lengths) and indirect hydrogeomorphic indices (e.g. specific upslope area, topographic wetness index) able to characterize the physical processes governing the landscape evolution (e.g. surface saturation, runoff, erosion, deposition). Such DEMs often contain artifacts and the automated hydrogeomorphic characterization of the watershed is influenced by terrain analysis procedures consisting in artificial depression (pit) and flat area treatment approaches combined with flow direction methods.In shallow landslide deterministic models, when applied using topographic dataset at medium scale (e.g. 30 m of resolution), the choice of the most suitable DEM-processing procedure is not trivial and can influence model results. This also affects the selection of most critical areas for further finer resolution studies or for the implementation of countermeasures aiming to landslide risk mitigation.In this paper such issue is investigated using as topographic input the ASTER DEMs and comparing two different combinations of DEM correction and flow routing schemes. The study areas comprise ten catchments in Italy for which hydrogeomorphic processes are significant. Aims of this paper are: 1) to introduce a parameter estimation procedure for the physically-based DEM correction method PEM4PIT (Physical Erosion Model for PIT removal); 2) to investigate the influence of different terrain analysis procedures on results of the slope stability model SHALSTAB (SHAllow Landsliding STABility) using remotely-sensed ASTER DEMs; 3) trying to assess which of terrain analysis methods is more appropriate for describing terrain instability.     

SRTM resample with short distance‐low nugget kriging

The shuttle radar topography mission (SRTM), was flow on the space shuttle Endeavour in February 2000, with the objective of acquiring a digital elevation model of all land between 60° north latitude and 56° south latitude, using interferometric synthetic aperture radar (InSAR) techniques. The SRTM data are distributed at horizontal resolution of 1 arc‐second (～30 m) for areas within the USA and at 3 arc‐second (～90 m) resolution for the rest of the world. A resolution of 90 m can be considered suitable for the small or medium‐scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it will not increase the level of detail of the original digital elevation model (DEM), but it will lead to a surface where there is the coherence of angular properties (i.e. slope, aspect) between neighbouring pixels, which is an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3” to 1”. We present for a test area in western USA, which includes different adjustment schemes (changes in nugget effect value and in the interpolation radius) and comparisons with the original 1” model of the area, with the national elevation dataset (NED) DEMs, and with other interpolation methods (splines and inverse distance weighted (IDW)). The basic concepts for using kriging to resample terrain data are: (i) working only with the immediate neighbourhood of the predicted point, due to the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; (ii) adding a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and; (iii) using a small value of nugget effect, to avoid smoothing that can obliterate terrain features. Drainages derived from the surfaces interpolated by kriging and by splines have a good agreement with streams derived from the 1” NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1” surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline‐interpolated surface still presented some noise and linear artifacts, which were removed by kriging.     

A universal spectral analytical method for digital terrain modeling

There are three major mathematical problems in digital terrain analysis: (1) interpolation of digital elevation models (DEMs); (2) DEM generalization and denoising; and (3) computation of morphometric variables through calculating partial derivatives of elevation. Traditionally, these three problems are solved separately by means of procedures implemented in different methods and algorithms. In this article, we present a universal spectral analytical method based on high-order orthogonal expansions using the Chebyshev polynomials of the first kind with the subsequent Fejér summation. The method is intended for the processing of regularly spaced DEMs within a single framework including DEM global approximation, denoising, generalization, as well as calculating the partial derivatives of elevation and local morphometric variables.

The method is exemplified by a portion of the Great Rift Valley and central Kenyan highlands. A DEM of this territory (the matrix 480 × 481 with a grid spacing of 30″) was extracted from the global DEM SRTM30_PLUS. We evaluated various sets of expansion coefficients (up to 7000) to approximate and reconstruct DEMs with and without the Fejér summation. Digital models of horizontal and vertical curvatures were computed using the first and second partial derivatives of elevation derived from the reconstructed DEMs. To evaluate the approximation accuracy, digital models of residuals (differences between the reconstructed DEMs and the initial one) were calculated. The test results demonstrated that the method is characterized by a good performance (i.e., a distinct monotonic convergence of the approximation) and a high speed of data processing. The method can become an effective alternative to common techniques of DEM processing.     

Special Issue on parallel processing in GIS

This paper explores three theoretical approaches for estimating the degree of correctness to which the accuracy figures of a gridded Digital Elevation Model (DEM) have been estimated depending on the number of checkpoints involved in the assessment process. The widely used average‐error statistic Mean Square Error (MSE) was selected for measuring the DEM accuracy. The work was focused on DEM uncertainty assessment using approximate confidence intervals. Those confidence intervals were constructed both from classical methods which assume a normal distribution of the error and from a new method based on a non‐parametric approach. The first two approaches studied, called Chi‐squared and Asymptotic Student t, consider a normal distribution of the residuals. That is especially true in the first case. The second case, due to the asymptotic properties of the t distribution, can perform reasonably well with even slightly non‐normal residuals if the sample size is large enough. The third approach developed in this article is a new method based on the theory of estimating functions which could be considered much more general than the previous two cases. It is based on a non‐parametric approach where no particular distribution is assumed. Thus, we can avoid the strong assumption of distribution normality accepted in previous work and in the majority of current standards of positional accuracy. The three approaches were tested using Monte Carlo simulation for several populations of residuals generated from originally sampled data. Those original grid DEMs, considered as ground data, were collected by means of digital photogrammetric methods from seven areas displaying differing morphology employing a 2 by 2 m sampling interval. The original grid DEMs were subsampled to generate new lower‐resolution DEMs. Each of these new DEMs was then interpolated to retrieve its original resolution using two different procedures. Height differences between original and interpolated grid DEMs were calculated to obtain residual populations. One interpolation procedure resulted in slightly non‐normal residual populations, whereas the other produced very non‐normal residuals with frequent outliers. Monte Carlo simulations allow us to report that the estimating function approach was the most robust and general of those tested. In fact, the other two approaches, especially the Chi‐squared method, were clearly affected by the degree of normality of the residual population distribution, producing less reliable results than the estimating functions approach. This last method shows good results when applied to the different datasets, even in the case of more leptokurtic populations. In the worst cases, no more than 64–128 checkpoints were required to construct an estimate of the global error of the DEM with 95% confidence. The approach therefore is an important step towards saving time and money in the evaluation of DEM accuracy using a single average‐error statistic. Nevertheless, we must take into account that MSE is essentially a single global measure of deviations, and thus incapable of characterizing the spatial variations of errors over the interpolated surface.     

Landform classification from a digital elevation model and satellite imagery

The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.     

Geomorphometric feature analysis using morphometric parameterization and artificial neural networks

This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detailed digital elevation models (DEMs) for all land masses between 60°N and 57°S. Exploiting these data for recognition and extraction of geomorphometric features is a challenging task. Results obtained with two methods, Wood's morphometric parameterization and the Self Organizing Map (SOM), are presented in this paper.Four morphometric parameters (slope, minimum curvature, maximum curvature and cross-sectional curvature) were derived by fitting a bivariate quadratic surface with a window size of 5 by 5 to the SRTM DEM. These parameters were then used as input to the two methods. Wood's morphometric parameterization provides point-based features (peak, pit and pass), line-based features (channel and ridge) and area-based features (planar). Since point-based features are defined as having a very small slope when their neighbors are considered, two tolerance values (slope tolerance and curvature tolerance) are introduced. Selection of suitable values for the tolerance parameters is crucial for obtaining useful results.The SOM as an unsupervised neural network algorithm is employed for the classification of the same morphometric parameters into ten classes characterized by morphometric position (crest, channel, ridge and plan area) subdivided by slope ranges. These terrain features are generic landform element and can be used to improve mapping and modeling of soils, vegetation, and land use, as well as ecological, hydrological and geomorphological features. These landform elements are the smallest homogeneous divisions of the land surface at the given resolution. The result showed that the SOM is an efficient scalable tool for analyzing geomorphometric features as meaningful landform elements, and uses the full potential of morphometric characteristics.     

SWAT分布式流域水文物理模型的改进及应用研究

SWAT (Soil and Water Assessment Tool) 模型是一个集成遥感 (RS)、地理信息系统 (GIS) 和数字高程模型(DEM)技术的先进的分布式流域水文物理模型。为了推动该模型在中国的适应性研究及应用,并改进模型以提高水文模拟的精度,针对模型在中国西北寒旱区的黑河流域和中西部温润的汉江流域的水文模拟中发现的问题进行了扩充和改进,增加了土壤粒径转换模块和天气发生器(WGEN)数据预处理模块,改进了模型中的WGEN算法、潜在蒸散量模拟算法以及气象参数的空间离散方法。利用扩充和改进后的模型对汉江褒河上游江口流域的降雨-径流过程进行了系统的研究。结果表明,不仅模型的使用效率有明显提高,而且改进后模型的效率系数和相关系数也比改进前有较大改善。     

SWAT模型在漳卫河流域应用研究

漳卫河流域是海河流域重要组成水系, 也是我国北方地区水资源短缺和水环境恶化的典 型区域之一。本文基于SWAT(Soil and Water Assessment Tool)构建漳卫河流域分布式水文模型, 对模型的几个重要参数进行敏感性分析, 总结出其取值变化对模拟结果的影响规律。并应用 2000~2004 年的水文气象数据, 进行分布式水文过程模拟, 将模拟结果与8 个行政区划的水资源 公报数据进行对比分析。结果显示模拟相对误差在10%以内, 能够为水资源管理提供重要参考。 研究成果对于SWAT 模型在国内的应用和推广, 具有很好的示范性。也为海河全流域分布式水文 模型的研制奠定了基础。     

Digital photogrammetry for air-photo-based construction of a digital elevation model over snow-covered areas ‐ Blamannsisen,Norway

Digital elevation models (DEMs) have been constructed over snow-covered and glaciated areas from 11 air photos optimized during production using a semi-automatic contrast-adjusting method called dodging. Construction of the DEMs from the air photos was accomplished using IMAGINE OrthoMAX software. In general, the results of the DEM construction are promising. Analytical and visual comparisons of GPS field data and published maps with the constructed DEMs indicate a high degree of agreement, with an average difference better than 2.8 m for most of the area. Errors and spurious data cells appear to have been minimized throughout most of the DEMs. One height anomaly, an erroneous peak with a height of between c. 250 m and 1200 m above the actual elevation, persistently appeared in one of the DEMs, despite numerous attempts to address the issue. Enhancements during the air-photo scanning process, such as sharpening and applying a tone curve, have been found to have a beneficial effect in reducing such erroneous results.     

基于DEM的黄土沟谷横剖面形态特征研究——以宜君、延安、绥德为例

选用1:10 000高精度5.0 m分辨率的DEM数据,在陕北地区,按黄土地貌演化序列次序,遴选分别代表黄土残塬沟壑、梁状丘陵沟壑、峁状丘陵沟壑地貌的宜君、延安、绥德3个流域,研究其中1 831个沟谷横剖面19个因子的形态特征,利用主成分分析法确立核心因子,分析结果表明：① 沟谷深度、宽度、横剖面面积、宽深比、侵蚀度与不对称性指标的主成分累积贡献率为95.02%,为黄土沟谷横剖面核心指标;② 随级别的增加,3个流域沟谷宽度、横剖面积、宽深比、侵蚀度均呈现总体增加的态势,与黄土地貌发育阶段具有明显的空间耦合性,尤其是宽深比与侵蚀度指标,反映出低级别黄土沟谷溯源侵蚀现象明显,以下切侵蚀为主,而高级别沟谷沟沿线后移,以侧向侵蚀拓宽为主的规律;③ 随级别增加沟谷深度呈现先增后降的态势,并在中级别沟谷出现拐点,与实地调研发现沟谷在中级别下切侵蚀遇到基岩的现象相吻合。     

青藏高原地形起伏度及其地理意义

地形起伏度是区域人居环境适宜性与资源环境承载力的关键评价指标之一。当前有关其最佳评价窗口、及其与海拔—相对高差的相互关系仍缺乏深入研究,进而影响该指标对区域地形起伏的有效表征。客观认识青藏高原地形起伏度有助于促进其国家生态安全屏障建设与区域绿色发展。以先进星载热发射和反射辐射仪全球数字高程模型（ASTER GDEM, 30 m）地形数据（V2）为基础,本文利用均值变点分析法确定了青藏高原地形起伏度评价的最佳分析窗口,基于地形起伏度模型（RDLS）研制了青藏高原首套30 m地形起伏度专题图,据此分析了地形起伏度与海拔、相对高差的相互关系,并界定了地形起伏度对区域地形起伏状况的有效表征。主要结果/结论包括：① 基于GDEM的青藏高原地形起伏度评价最佳窗口为41×41个像元的矩形邻域,对应面积约为1.51 km²,均值变点分析表明区域地形起伏度评价最佳窗口有其唯一性。② 青藏高原地形起伏度均值约为5.06,超3/5区域地形起伏度介于4.5~5.7之间;整体上,青藏高原地形起伏程度由其东北部向西南部、西部递增,仅在柴达木盆地、藏南谷地以及河湟谷地出现低起伏地貌特征。且地表起伏在不同纬度剖面变化较为一致（沿山脉走向）,但不同经度剖面起伏层次错落（横切山脉走向）。③ 相关性分析表明不同地形起伏度分别对应不同平均海拔、不同相对高差的地貌单元。青藏高原地形起伏度经纬向剖面分析表明,该区由东部的低山稳步爬升,山体经历骤然爬升（即地表起伏特征剧烈）后形成以极高山为主的有序错落起伏（喜马拉雅山脉）。     

Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

An iterative procedure that implements the classification of continuous topography as a problem in digital image-processing automatically divides an area into categories of surface form; three taxonomic criteria–slope gradient, local convexity, and surface texture–are calculated from a square-grid digital elevation model (DEM). The sequence of programmed operations combines twofold-partitioned maps of the three variables converted to greyscale images, using the mean of each variable as the dividing threshold. To subdivide increasingly subtle topography, grid cells sloping at less than mean gradient of the input DEM are classified by designating mean values of successively lower-sloping subsets of the study area (nested means) as taxonomic thresholds, thereby increasing the number of output categories from the minimum 8 to 12 or 16. Program output is exemplified by 16 topographic types for the world at 1-km spatial resolution (SRTM30 data), the Japanese Islands at 270 m, and part of Hokkaido at 55 m. Because the procedure is unsupervised and reflects frequency distributions of the input variables rather than pre-set criteria, the resulting classes are undefined and must be calibrated empirically by subsequent analysis. Maps of the example classifications reflect physiographic regions, geological structure, and landform as well as slope materials and processes; fine-textured terrain categories tend to correlate with erosional topography or older surfaces, coarse-textured classes with areas of little dissection. In Japan the resulting classes approximate landform types mapped from airphoto analysis, while in the Americas they create map patterns resembling Hammond's terrain types or surface-form classes; SRTM30 output for the United States compares favorably with Fenneman's physical divisions. Experiments are suggested for further developing the method; the Arc/Info AML and the map of terrain classes for the world are available as online downloads.     

老挝VIIRS活跃火的主要自然地理要素特征

利用美国国家航空航天局火灾信息资源管理系统（FIRMS）VIIRS V1活跃火、先进星载热发射和反射辐射仪全球数字高程模型（ASTER GDEM）、MODIS NDVI/LSWI与土地覆被数据产品（FROM-GLC）,基于GIS定量分析老挝2012—2017年从分省到国家不同空间尺度活跃火频次的动态变化,并重点分析2015厄尔尼诺年活跃火频次及其与海拔、坡度、植被-水分（NDVI ^*、LSWI）指数、土地覆被等主要自然地理要素的相关特征。结果表明：① 老挝活跃火频次呈先增后减的趋势,峰值在2015厄尔尼诺年,主要集中在上、中寮,其中琅勃拉邦、沙耶武里与沙湾拿吉三省居前三位,而万象市、塞公和赛宋本则居后三位。② 老挝2012—2017年活跃火发生频次与地形因素（海拔、坡度）的关系基本相同,活跃火集中在海拔1000 m以下、坡度小于30°的低山-丘陵区。其中,上寮活跃火集中分布在500~1000 m、10~30°的斜陡坡山地,中、下寮集中在500 m以下、2~20°的缓斜坡丘陵。③ 活跃火高度集中在旱季,以3月、4月最多,并集中分布在NDVI为0.4~0.8和LSWI为0.2~0.6的中高覆盖度植被区。④ 老挝活跃火主要发生在森林这一土地覆被类型中,且以上寮最为集中,而中寮多以农田、草地和灌丛活跃火为主。综上,基于自然地理要素的活跃火特征分析可以有效识别其发生类型,即老挝活跃火主要由刀耕火种农业所引起。     

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.     

A comparison of digital elevation models generated from different data sources

It can be challenging to accurately determine the topography of physically complex landscapes in remote areas. Ground-based surveys can be difficult, time consuming and may miss significant elements of the landscape. This study compares digital elevation models (DEMs) generated from three different data sources, of the physically complex Narran Lakes Ecosystem, a major floodplain wetland ecosystem in Australia. Topographic surfaces were generated from an airborne laser altimetry (LiDAR) survey, a ground-based differential GPS (DGPS) survey containing more than 20,000 points, and the 9″ DEM of Australia. The LiDAR- and DGPS-derived data generated a more thorough DEM than the 9″ DEM; however, LiDAR generated a surface topography that yielded significantly more detail than the DGPS survey, with no noticeable loss of elevational accuracy. Both the LiDAR- and the DGPS-derived DEMs compute the overall surface area and volume of the largest floodplain lake within the system to within 1% of each other. LiDAR is shown to be a highly accurate and robust technique for acquiring large quantities of topographic data, even in locations that are unsuitable for ground surveying and where the overall landscape is of exceptionally low relief. The results of this study highlight the potential for LiDAR surveys in the accurate determination of the topography of floodplain wetlands. These data can form an important component of water resource management decisions, particularly where environmental water allocations for these important ecosystems need to be determined.     

Effects of lidar post‐spacing and DEM resolution to mean slope estimation

This paper examines the effect of scale (exhibited by spatial sampling) in modeling mean slope from lidar data using two representations of scale: lidar posting density (i.e. post‐spacing) and DEM resolution (i.e. cell size). The study areas selected include six small (i.e. approximately 3 km²) urban drainage basins in Richland County, SC, USA, which share similar hydrologic characteristics. This research spatially sampled an airborne lidar dataset collected in 2000 at a 2 m nominal posting density to simulate lidar posting density at various post‐spacings, from 2 m through 10 m. DEMs were created from the lidar observations at a corresponding cell size using spatial interpolation. Finally, using these DEMs, a sensitivity analysis between modeled terrain slope and lidar post‐spacing was conducted. Results of the sensitivity analyses showed that the deviation between mean slope and modeled mean slope decreases with finer posting density and DEM resolution. The relationship of mean slope with varying cell sizes and post‐spacing suggests a linear and a logarithmic function, respectively, for all study areas. More importantly, cell size has a greater effect on mean slope than lidar posting density. Implications of these results for lumped hydrologic modeling are then postulated.     

Interannual and seasonal variations in energy and carbon exchanges over the larch forests on the permafrost in northeastern Mongolia

The larch forests on the permafrost in northeastern Mongolia are located at the southern limit of the Siberian taiga forest, which is one of the key regions for evaluating climate change effects and responses of the forest to climate change. We conducted long-term monitoring of seasonal and interannual variations in hydrometeorological elements, energy, and carbon exchange in a larch forest (48°15′24′′N, 106°51′3′′E, altitude: 1338 m) in northeastern Mongolia from 2010 to 2012. The annual air temperature and precipitation ranged from −0.13 °C to −1.2 °C and from 230 mm to 317 mm. The permafrost was found at a depth of 3 m. The dominant component of the energy budget was the sensible heat flux (H) from October to May (H/available energy [R_a] = 0.46; latent heat flux [LE]/R_a = 0.15), while it was the LE from June to September (H/R_a = 0.28, LE/R_a = 0.52). The annual net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RE) were −131 to −257 gC m⁻² y⁻¹, 681–703 gC m⁻² y⁻¹, and 423–571 gC m⁻² y⁻¹, respectively. There was a remarkable response of LE and NEE to both vapor pressure deficit and surface soil water content.