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.     

Alteration of hydrological processes and streamflow with juniper (Juniperus virginiana) encroachment in a mesic grassland catchment

To understand the effect of woody plant encroachment on hydrological processes of mesic grasslands, we quantified infiltration capacity in situ, the temporal changes in soil water storage, and streamflow of a grassland catchment and a catchment heavily encroached by juniper (Juniperus virginiana, eastern redcedar) in previously cultivated, non‐karst substrate grasslands in north‐central Oklahoma for 3 years. The initial and steady‐state infiltration rates under the juniper canopy were nearly triple to that of the grassland catchment and were intermediate in the intercanopy spaces within the encroached catchment. Soil water content and soil water storage on the encroached catchment were generally lower than on the grassland catchment, especially when preceding the seasons of peak rainfall in spring and fall. Frequency and magnitude of streamflow events were reduced in the encroached catchment. Annual runoff coefficients for the encroached catchment averaged 2.1%, in contrast to 10.6% for the grassland catchment. Annual streamflow duration ranged from 80 to 250 h for the encroached catchment compared with 600 to 800 h for the grassland catchment. Our results showed that the encroachment of juniper into previously cultivated mesic grasslands fundamentally alters catchment hydrological function. Rapid transformation of mesic grassland to a woodland state with juniper encroachment, if not confined, has the potential to drastically reduce soil water, streamflow and flow duration of ephemeral streams in the Southern Great Plains. Copyright © 2013 John Wiley & Sons, Ltd.     

Dynamic river–groundwater exchange in the presence of a saline,semi‐confined aquifer

Understanding groundwater–surface water exchange in river banks is crucial for effective water management and a range of scientific disciplines. While there has been much research on bank storage, many studies assume idealized aquifer systems. This paper presents a field‐based study of the Tambo Catchment (southeast Australia) where the Tambo River interacts with both an unconfined aquifer containing relatively young and fresh groundwater (<500 μS/cm and <100 years old) and a semi‐confined artesian aquifer containing old and saline groundwater (electrical conductivity > 2500 μS/cm and >10 000 years old). Continuous groundwater elevation and electrical conductivity monitoring within the different aquifers and the river suggest that the degree of mixing between the two aquifers and the river varies significantly in response to changing hydrological conditions. Numerical modelling using MODFLOW and the solute transport package MT3DMS indicates that saline water in the river bank moves away from the river during flooding as hydraulic gradients reverse. This water then returns during flood recession as baseflow hydraulic gradients are re‐established. Modelling also indicates that the concentration of a simulated conservative groundwater solute can increase for up to ~34 days at distances of 20 and 40 m from the river in response to flood events approximately 10 m in height. For the same flood event, simulated solute concentrations within 10 m of the river increase for only ~15 days as the infiltrating low‐salinity river water drives groundwater dilution. Average groundwater fluxes to the river stretch estimated using Darcy's law were 7 m³/m/day compared with 26 and 3 m³/m/day for the same periods via mass balance using Radon (²²²Rn) and chloride (Cl), respectively. The study shows that by coupling numerical modelling with continuous groundwater–surface water monitoring, the transient nature of bank storage can be evaluated, leading to a better understanding of the hydrological system and better interpretation of hydrochemical data. Copyright © 2015 John Wiley & Sons, Ltd.     

Inverse modelling using PS‐InSAR data for improved land subsidence simulation in Las Vegas Valley,Nevada

Las Vegas Valley has had a long history of groundwater development and subsequent surface deformation. InSAR interferograms have revealed detailed and complex spatial patterns of subsidence in the Las Vegas Valley area that do not coincide with major pumping regions. This research represents the first effort to use high spatial and temporal resolution subsidence observations from InSAR and hydraulic head data to inversely calibrate transmissivities (T), elastic and inelastic skeletal storage coefficients (S_ke and S_kv) of the developed‐zone aquifer and conductance (CR) of the basin‐fill faults for the entire Las Vegas basin. The results indicate that the subsidence observations from InSAR are extremely beneficial for accurately quantifying hydraulic parameters, and the model calibration results are far more accurate than when using only groundwater levels as observations, and just a limited number of subsidence observations. The discrepancy between distributions of pumping and greatest levels of subsidence is found to be attributed to spatial variations in clay thickness. The Eglington fault separates thicker interbeds to the northwest from thinner interbeds to the southeast and the fault may act as a groundwater‐flow barrier and/or subsidence boundary, although the influence of the groundwater barrier to this area is found to be insignificant. Copyright © 2016 John Wiley & Sons, Ltd.     

Scales and magnitude of hyporheic,river–aquifer and bank storage exchange fluxes

Many studies have investigated the exchange processes that occur between rivers and groundwater systems and have successfully quantified the water fluxes involved. Specifically, these exchange processes include hyporheic exchange, river–aquifer exchange (groundwater discharge and river loss) and bank storage exchange. Remarkably, there are relatively few examples of field studies where more than one exchange process is quantified, and as a consequence, the relationships between them are not well understood. To compare the relative magnitudes of these common exchange processes, we have collected data from 54 studies that have quantified one or more of these exchange flux types. Each flux value is plotted against river discharge at the time of measurement to allow the different exchange flux types to be compared. We show that there are positive relationships between the magnitude of each exchange flux type and increasing river discharge across the different studies. For every one order of magnitude increase in river discharge, the hyporheic, river–aquifer and bank storage exchange fluxes increase by factors of 2.7, 2.9 and 2.5, respectively. On average, hyporheic exchange fluxes are almost an order of magnitude greater than river–aquifer exchange fluxes, which are, in turn, approximately four times greater than bank storage exchange fluxes for the same river discharge. Unless measurement approaches that can distinguish between different types of exchange flux are used, there is potential for hyporheic exchange fluxes to be misinterpreted as river–aquifer exchange fluxes, with possible implications for water resource management decisions. Copyright © 2015 John Wiley & Sons, Ltd.     

On the relationship between historical land‐use change and water availability: the case of the lower Tarim River region in northwestern China

Natural ecosystems in the region of the lower Tarim River in northwestern China strongly deteriorated since the 1950s due to an expanding desertification. As a result, the downstream Tarim River reaches became permanently dry land. This historical evolution in land‐use change is typically the result of the anthropogenic impact on natural ecosystems. On the basis of a spatially distributed hydrological catchment model bidirectionally linked with a fully hydrodynamic MIKE11 river model, land‐use changes characterized by historical changes in leaf area index (LAI) of vegetation, as well as the evolution of irrigated surface areas, can be causally related to changes in water resources (groundwater storage and surface water resources). An increased surface area of irrigated (agricultural) land, together with a majority of inefficient irrigation methods, did lead to a strong increase of water resources consumption of the farmlands located in the upper Tarim River area. Evidently, this evolution influenced available water resources downstream in the Tarim basin. As a result, farmland has been gradually relocated to the upstream regions. This has led to reduced flows from the upper Tarim stream, which subsequently accelerated the dropping of the groundwater level downstream in the basin. This study moreover demonstrates that land surface biomass changes (cumulative LAI) along the lower Tarim River are strongly related to the changes in groundwater storage. Copyright © 2012 John Wiley & Sons, Ltd.     

应急地理信息数据管理探讨

应急地理信息作为地理信息的重要组成部分,涉及众多行业部门,要想有效地利用应急地理信息资源,对应急地理信息数据管理的研究则非常重要。本文以应急地理信息的分类、资源目录管理、采集与入库、授权与共享为主要研究内容,结合数据管理原型对研究内容进行了实践开发。为建设应急地理信息服务平台积累了经验,为政府应急部门快速利用应急信息分析研判、处置应急事件提供了技术支撑。     

2000～2015年会理县地表覆盖变化监测

以LandSat多时相影像为数据源,采用基于规则的面向对象分类方法,监测会理县2000～ 2015年地表覆盖变化.结合1∶50 000 DEM数据,分析了会理县地表覆盖构成与转移、变化动态度及潜在退耕地情况.监测分析结论能为会理县加强空间管制、促进土地资源集约利用、指导生态文明建设等提供决策信息支撑.     

求解GM(1,1)模型新总体最小二乘算法

在GM(1,1)模型中系数矩阵和观测向量都是由原始序列组成的。系数矩阵中同样是有误差的,与观测向量中的误差一样,亦来源于原始序列,即它们误差同源。不同位置的相同元素应该有相同的改正数,采用传统总体最小二乘求解则不能达到此目的。针对这一缺陷,推导了一种新的总体最小二乘算法;并且通过算例验证了新方法的可行性和有效性。     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。