利用可见光/近红外反射光谱估算土壤总氮含量的实验研究

利用土壤的室内反射率光谱，探讨土壤氮元素的高光谱机理。利用土壤光谱各吸收带的特征参数与总氮含量进行逐步回归运算，确定与氮元素关系比较密切的几个吸收带。计算出这几个特征吸收带内土壤反射率的变化形式：一阶导数(FDR)、倒数(1/R)、倒数之对数(log(1/R))、波段深度(Depth)，并与总氮含量进行逐步回归分析，得到比较理想的结果：建模样本的Ra^2(修正的判定系数)分别为0．789、0．753、0．736、0．699，验证样本的Ra^2分别为0．759、0．468、0．794、0．725。可见土壤的反射率光谱与氮元素含量之间存在比较明显的相关性，可见光／近红外反射光谱具有快速估算土壤中氮元素含量的潜力。     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

网络大数据下的中国现代食甜习惯空间分布特征及其影响因素研究

饮食地理文化作为地域文化中最具地方特色的重要元素,在现代人口大规模流动背景下呈现出全新的多样化局面,而基于传统认知的“南甜北咸”的地域分异已然不能代表中国现代食甜分布的空间特征。因此,本文采用网络爬虫技术,获取我国大陆31个省会城市共计约2000万条美食消费数据,从传统类菜品、主食类菜品、饮料类和甜品类菜品4个方面计算城市食甜度,在ArcGIS、MySQL软件支持下,借助GIS空间分析和数理统计方法探究我国现代食甜习惯的空间分布特征,分析影响食甜分布的因素。研究发现：① 中国食甜在空间分布上存在显著的地域分异特征,聚类分析评价参数R ²高达0.88,现代食甜习惯总体呈现“东高北中,西微内低”的包围式格局;② 从整体抑或局部角度,在1%显著性水平上莫兰指数均为正,中国食甜分布呈现显著的空间正相关关系,形成特色鲜明的3个地理集聚区,即以苏浙沪闽为主的东南沿海高甜集聚区,以渝黔川为主的西南内陆低甜集聚区和以陕宁为主的西北内陆低甜集聚区;③ 构建了中国现代食甜习惯分布影响因素模型,其拟合精度为0.82,分析结果显示降水、湿度、气温等气象要素及地理位置是影响现代我国食甜空间分布的重要因素。     

Micromechanical approach to swelling behavior of capillary-porous media with coupled physics

A detailed multiscale analysis is presented of the swelling phenomenon in unsaturated clay-rich materials in the linear regime through homogenization. Herein, the structural complexity of the material is formulated as a three-scale, triple porosity medium within which microstructural information is transmitted across the various scales, leading ultimately to an enriched stress-deformation relation at the macroscopic scale. As a side note, such derived relationship leads to a tensorial stress partitioning that is reminiscent of a Terzaghi-like effective stress measure. Otherwise, a major result that stands out from previous works is the explicit expression of swelling stress and capillary stress in terms of micromechanical interactions at the very fine scale down to the clay platelet level, along with capillary stress emerging due to interactions between fluid phases at the different scales, including surface tension, pore size, and morphology. More importantly, the swelling stress is correlated with the disjoining forces due to electrochemical effects of charged ions on clay minerals and van der Waals forces at the nanoscale. The resulting analytical expressions also elucidate the role of the various physics in the deformational behavior of clayey material. Finally, the capability of the proposed formulation in capturing salient behaviors of unsaturated expansive clays is illustrated through some numerical examples.     

Constitutive modelling of fine-grained gassy soil: A composite approach

Fine-grained marine sediments containing large undissolved gas bubbles are widely distributed around the world. Presence of the bubbles could degrade the undrained shear strength (s_u ) of the soil, when the gas pressure u_g is relatively high as compared with the effective stress in the saturated soil matrix. Meanwhile, the addition of bubbles may also increase s_u when the difference between u_g and pore water pressure u_w becomes smaller than the water entry value, causing partial water drainage from the saturated matrix into the bubbles (bubble flooding) during globally undrained shearing. A new constitutive model for describing the two competing effects on the stress-strain relationship of fine-grained gassy soil is proposed within the framework of critical state soil mechanics. The gassy soil is considered as a three-phase composite material with compressible cavities, which allows water entry from the saturated matrix. Bubble flooding is modelled by introducing an additional positive volumetric strain increment of the saturated clay matrix, which is dependent on the difference between pore gas and pore water pressure based on experimental observations. A modified hardening law based on that of the modified Cam clay model is employed, which in conjunction with the expression for bubble flooding, can describe both the detrimental and beneficial effects of gas bubbles on soil strength and plastic hardening in shear. Only two extra parameters in addition to those in the modified Cam clay model are used. It is shown that the key features of the stress-strain relationship of three fine-grained gassy soils can be reproduced satisfactorily.     

Calibration of paired watersheds: Utility of moving sums in presence of externalities

Historically, paired watershed studies have been used to quantify the hydrological effects of land use and management practices by concurrently monitoring 2 similar watersheds during calibration (pretreatment) and post‐treatment periods. This study characterizes seasonal water table and flow response to rainfall during the calibration period and tests a change detection technique of moving sums of recursive residuals (MOSUM) to select calibration periods for each control–treatment watershed pair when the regression coefficients for daily water table elevation were most stable to minimize regression model uncertainty. The control and treatment watersheds were 1 watershed of 3–4‐year‐old intensely managed loblolly pine (Pinus taeda L.) with natural understory, 1 watershed of 3–4‐year‐old loblolly pine intercropped with switchgrass (Panicum virgatum), 1 watershed of 14–15‐year‐old thinned loblolly pine with natural understory (control), and 1 watershed of switchgrass only. The study period spanned from 2009 to 2012. Silvicultural operational practices during this period acted as external factors, potentially shifting hydrologic calibration relationships between control and treatment watersheds. MOSUM results indicated significant changes in regression parameters due to silvicultural operations and were used to identify stable relationships for water table elevation. None of the calibration relationships developed using this method were significantly different from the classical calibration relationship based on published historical data. We attribute that to the similarity of historical and 2010–2012 leaf area index on control and treatment watersheds as moderated by the emergent vegetation. Although the MOSUM approach does not eliminate the need for true calibration data or replace the classic paired watershed approach, our results show that it may be an effective alternative approach when true data are unavailable, as it minimizes the impacts of external disturbances other than the treatment of interest.     

Concentration–discharge relationships in the coal mined region of the New River basin and Indian Fork sub‐basin,Tennessee, USA

For many basins, identifying changes to water quality over time and understanding current hydrologic processes are hindered by fragmented and discontinuous water‐quality and hydrology data. In the coal mined region of the New River basin and Indian Fork sub‐basin, muted and pronounced changes, respectively, to concentration–discharge (C–Q) relationships were identified using linear regression on log‐transformed historical (1970s–1980s) and recent (2000s) water‐quality and streamflow data. Changes to C–Q relationships were related to coal mining histories and shifts in land use. Hysteresis plots of individual storms from 2007 (New River) and the fall of 2009 (Indian Fork) were used to understand current hydrologic processes in the basins. In the New River, storm magnitude was found to be closely related to the reversal of loop rotation in hysteresis plots; a peak‐flow threshold of 25 cubic meters per second (m³/s) segregates hysteresis patterns into clockwise and counterclockwise rotational groups. Small storms with peak flow less than 25 m³/s often resulted in dilution of constituent concentrations in headwater tributaries like Indian Fork and concentration of constituents downstream in the mainstem of the New River. Conceptual two or three component mixing models for the basins were used to infer the influence of water derived from spoil material on water quality. Copyright © 2012 John Wiley & Sons, Ltd.