Seasonal connections between meteoric water and streamflow generation along a mountain headwater stream

In snowmelt-driven mountain watersheds, the hydrologic connectivity between meteoric waters and stream flow generation varies strongly with the season, reflecting variable connection to soil and groundwater storage within the watershed. This variable connectivity regulates how streamflow generation mechanisms transform the seasonal and elevational variation in oxygen and hydrogen isotopic composition (δ¹⁸O and δD) of meteoric precipitation. Thus, water isotopes in stream flow can signal immediate connectivity or more prolonged mixing, especially in high-relief mountainous catchments. We characterized δ¹⁸O and δD values in stream water along an elevational gradient in a mountain headwater catchment in southwestern Montana. Stream water isotopic compositions related most strongly to elevation between February and March, exhibiting higher δ¹⁸O and δD values with decreasing elevation. These elevational isotopic lapse rates likely reflect increased connection between stream flow and proximal snow-derived water sources heavily subject to elevational isotopic effects. These patterns disappeared during summer sampling, when consistently lower δ¹⁸O and δD values of stream water reflected contributions from snowmelt or colder rainfall, despite much higher δ¹⁸O and δD values expected in warmer seasonal rainfall. The consistently low isotopic values and absence of a trend with elevation during summer suggest lower connectivity between summer precipitation and stream flow generation as a consequence of drier soils and greater transpiration. As further evidence of intermittent seasonal connectivity between the stream and adjacent groundwaters, we observed a late-winter flush of nitrate into the stream at higher elevations, consistent with increased connection to accumulating mineralized nitrogen in riparian wetlands. This pattern was distinct from mid-summer patterns of nitrate loading at lower elevations that suggested heightened human recreational activity along the stream corridor. These observations provide insights linking stream flow generation and seasonal water storage in high elevation mountainous watersheds. Greater understanding of the connections between surface water, soil water and groundwater in these environments will help predict how the quality and quantity of mountain runoff will respond to changing climate and allow better informed water management decisions.     

一种基于自证明公钥的可公开验证的认证加密方案

针对现有的基于证书的认证加密方案通信量和计算量大的缺点,提出了基于自证明公钥的可公开验证的认证加密方案。详细阐述了方案的系统初始化、签名生成、消息恢复验证以及公开验证4个阶段,方案的安全性是基于求解离散对数的困难性(DLP)和强单向hash函数(OWFH)的不可逆。该方案在没有签名者的协作下,任何验证者都可验证签名者的诚实性,在验证签名的同时可认证签名者的公钥,因而减少了存储空间、通信量和计算量。     

Virgin rock temperatures from well logs — accuracy analysis for some advanced inversion models

Temperature data from 18 measurement series obtained during logging of the Oseberg field in the northern North Sea are presented. Because the measurement series are taken at approximately the same depth, they should give identical temperatures after depth correction, and are suitable for assessing the performance of different models used to determine virgin rock temperatures from well log information.We have used this data set to test the properties of the different models given by Shen and Beck (1986). Although these models were built to simulate closely the thermal recovery of a well and are unbiased, the uncertainties in the temperature estimates when applied to real data are found to be no less than those from simpler (biased) models. This fact confirms the conclusion of Hermanrud (1989a) who showed that physical factors other than those presently accounted for significantly influence the thermal recovery of a borehole.     

Community turnover provides insight into variable invertebrate recovery between restored streams with different integrated catchment management plans

ABSTRACT

Understanding temporal patterns in restored environments is important for identifying potential barriers to recovery and improved management of degraded habitats. In this paper, we use temporal beta diversity analyses to compare invertebrate community recovery trajectories in three restored agricultural stream sites under different integrated catchment plans, a native forest reference site, and two unmodified pasture control stream sites over 24 years. The restored sites diverged from their initial community composition over time and became more similar to the reference site community, which was relatively stable over time. Variation partitioning showed that prior to restoration beta diversity was primarily associated with environmental and spatial drivers, whereas post-restoration beta diversity was more influenced by temporal and environmental drivers, including changes in substrate size, fine sediments, water clarity, and nutrients, as well as temperature and flow regime. Species’ contributions to beta diversity varied between sites and years, with sensitive EPT taxa contributing more in reference and control sites. However, contributions of some EPT species, particularly mayflies, increased in restored sites post-ICM. In summary, after nearly two decades of ICM, restored stream sites show recovery towards reference conditions, yet differences persist, indicating that rehabilitation may take longer, depending on the restoration goals.     

遥感和GIS支持下的分布式融雪径流过程模拟研究

基于遥感(RS)和地理信息系统(GIS)技术,设计和构建了一个分布式融雪径流模型,整个分布式融雪径流过程的模拟计算基于能量平衡和水量平衡,由分布式栅格融雪过程、分布式栅格产流过程以及分布式栅格汇流过程组成,融雪以及产汇流过程全部基于栅格尺度,全面实现了融雪过程的分布式模拟.分布式栅格融雪过程中对"度日法"加以改进,引入了"单元时段"的概念,从而得到了"度分融雪模型";针对融雪过程中颇为复杂的冻融反复性难题,提出了旨在解释积雪冻融反复性物理机制的"冻融系数"的重要概念,对于准确把握融雪过程的物理机制具有重要意义.同时基于GIS开发了分布式融雪径流模拟系统,为分布式融雪径流模型的运行提供了平台和技术支持,二者均为融雪洪水预警决策支持系统的核心模块部分.基于由MODlS等遥感数据得到的积雪信息、地表温度等下垫面信息,基础地理信息数据如DEM及其空间分析数据和大量野外同步观测数据(积雪信息、气象数据),对典型研究区新疆军塘湖流域2006年春季典型融雪期(2006-03-06,11:00-2006-03-10,11:00)内的洪水过程进行了模拟,模拟结果精度较高,平均精度0.82,达到了融雪洪水预警预报的业务需求标准.     

The GeoForschungsZentrum Potsdam/Groupe de Recherche de Gèodésie Spatiale satellite-only and combined gravity field models: EIGEN-GL04S1 and EIGEN-GL04C

The recent improvements in the Gravity Recovery And Climate Experiment (GRACE) tracking data processing at GeoForschungsZentrum Potsdam (GFZ) and Groupe de Recherche de Géodésie Spatiale (GRGS) Toulouse, the availability of newer surface gravity data sets in the Arctic, Antarctica and North-America, and the availability of a new mean sea surface height model from altimetry processing at GFZ gave rise to the generation of two new global gravity field models. The first, EIGEN-GL04S1, a satellite-only model complete to degree and order 150 in terms of spherical harmonics, was derived by combination of the latest GFZ Potsdam GRACE-only (EIGEN-GRACE04S) and GRGS Toulouse GRACE/LAGEOS (EIGEN-GL04S) mean field solutions. The second, EIGEN-GL04S1 was combined with surface gravity data from altimetry over the oceans and gravimetry over the continents to derive a new high-resolution global gravity field model called EIGEN-GL04C. This model is complete to degree and order 360 and thus resolves geoid and gravity anomalies at half- wavelengths of 55 km at the equator. A degree-dependent combination method has been applied in order to preserve the high accuracy from the GRACE satellite data in the lower frequency band of the geopotential and to form a smooth transition to the high-frequency information coming from the surface data. Compared to pre-CHAMP global high-resolution models, the accuracy was improved at a spatial resolution of 200 km (half-wavelength) by one order of magnitude to 3 cm in terms of geoid heights. The accuracy of this model (i.e. the commission error) at its full spatial resolution is estimated to be 15 cm. The model shows a reduced artificial meridional striping and an increased correlation of EIGEN-GL04C-derived geostrophic meridional currents with World Ocean Atlas 2001 (WOA01) data. These improvements have led to select EIGEN-GL04C for JASON-1 satellite altimeter data reprocessing. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Methodology and use of tensor invariants for satellite gravity gradiometry

Although its use is widespread in several other scientific disciplines, the theory of tensor invariants is only marginally adopted in gravity field modeling. We aim to close this gap by developing and applying the invariants approach for geopotential recovery. Gravitational tensor invariants are deduced from products of second-order derivatives of the gravitational potential. The benefit of the method presented arises from its independence of the gradiometer instrument’s orientation in space. Thus, we refrain from the classical methods for satellite gravity gradiometry analysis, i.e., in terms of individual gravity gradients, in favor of the alternative invariants approach. The invariants approach requires a tailored processing strategy. Firstly, the non-linear functionals with regard to the potential series expansion in spherical harmonics necessitates the linearization and iterative solution of the resulting least-squares problem. From the computational point of view, efficient linearization by means of perturbation theory has been adopted. It only requires the computation of reference gravity gradients. Secondly, the deduced pseudo-observations are composed of all the gravitational tensor elements, all of which require a comparable level of accuracy. Additionally, implementation of the invariants method for large data sets is a challenging task. We show the fundamentals of tensor invariants theory adapted to satellite gradiometry. With regard to the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite gradiometry mission, we demonstrate that the iterative parameter estimation process converges within only two iterations. Additionally, for the GOCE configuration, we show the invariants approach to be insensitive to the synthesis of unobserved gravity gradients.     

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure. Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development stage, the LSQR method appears ready to deal with real-data applications.