Nonrainfall water origins and formation mechanisms

Dryland ecosystems cover 40% of the total land surface on Earth and are defined broadly as zones where precipitation is considerably less than the potential evapotranspiration. Nonrainfall waters (for example, fog and dew) are the least-studied and least-characterized components of the hydrological cycle, although they supply critical amounts of water for dryland ecosystems. The sources of nonrainfall waters are largely unknown for most systems. In addition, most field and modeling studies tend to consider all nonrainfall inputs as a single category because of technical constraints, which hinders prediction of dryland responses to future warming conditions. This study uses multiple stable isotopes (2H, 18O, and 17O) to show that fog and dew have multiple origins and that groundwater in drylands can be recycled via evapotranspiration and redistributed to the upper soil profile as nonrainfall water. Surprisingly, the non–ocean-derived (locally generated) fog accounts for more than half of the total fog events, suggesting a potential shift from advection-dominated fog to radiation-dominated fog in the fog zone of the Namib Desert. This shift will have implications on the flora and fauna distribution in this fog-dependent system. We also demonstrate that fog and dew can be differentiated on the basis of the dominant fractionation (equilibrium and kinetic) processes during their formation using the 17O-18O relationship. Our results are of great significance in an era of global climate change where the importance of nonrainfall water increases because rainfall is predicted to decline in many dryland ecosystems. Fog and dew in the Namib Desert have multiple origins and their formation can be differentiated using stable isotopes. Fog and dew in the Namib Desert have multiple origins and their formation can be differentiated using stable isotopes

Global synthesis of vegetation control on evapotranspiration partitioning

Author's manuscript made available in accordance with the publisher's policy.Evapotranspiration (ET) is an important component of the global hydrological cycle. However, to what extent transpiration ratios (T/ET) are controlled by vegetation and the mechanisms of global-scale T/ET variations are not clear. We synthesized all the published papers that measured at least two of the three components (E, T, and ET) and leaf area index (LAI) simultaneously. Nonlinear relationships between T/ET and LAI were identified for both the overall data set and agricultural or natural data subsets. Large variations in T/ET occurred across all LAI ranges with wider variability at lower LAI. For a given LAI, higher T/ET was observed during later vegetation growing stage within a season. We developed a function relating T/ET to the growing stage relative to the timing of peak LAI. LAI and growing stage collectively explained 43% of the variations in the global T/ET data set, providing a new way to interpret and model global T/ET variability

Systematically improvable optimized atomic basis sets for {\it ab inito} calculations

We propose a unique scheme to construct fully optimized atomic basis sets for density-functional calculations. The shapes of the radial functions are optimized by minimizing the {\it spillage} of the wave functions between the atomic orbital calculations and the converged plane wave calculations for dimer systems. The quality of the bases can be systematically improved by increasing the size of the bases within the same framework. The scheme is easy to implement and very flexible. We have done extensive tests of this scheme for wide variety of systems. The results show that the obtained atomic basis sets are very satisfactory for both accuracy and transferability

Stable Isotopes of Water Vapor in the Vadose Zone: A Review of Measurement and Modeling Techniques

Author's manuscript made available in accordance with the publisher's policy.The stable isotopes of soil water vapor can be useful in the study of ecosystem processes. Modeling has historically dominated the measurement of these parameters due to sampling difficulties. We discuss new developments in modeling and measurement, including the implications of including soil water potential in the Craig–Gordon modeling framework. The stable isotopes of soil water vapor are useful tracers of hydrologic processes occurring in the vadose zone. The measurement of soil water vapor isotopic composition (δ18O, δ2H) is challenging due to difficulties inherent in sampling the vadose zone airspace in situ. Historically, these parameters have therefore been modeled, as opposed to directly measured, and typically soil water vapor is treated as being in isotopic equilibrium with liquid soil water. We reviewed the measurement and modeling of soil water vapor isotopes, with implications for studies of the soil–plant–atmosphere continuum. We also investigated a case study with in situ measurements from a soil profile in a semiarid African savanna, which supports the assumption of liquid–vapor isotopic equilibrium. A contribution of this work is to introduce the effect of soil water potential (Ѱ) on kinetic fractionation during soil evaporation within the Craig–Gordon modeling framework. Including Ѱ in these calculations becomes important for relatively dry soils (Ѱ < −10 MPa). Additionally, we assert that the recent development of laser-based isotope analytical systems may allow regular in situ measurement of the vadose zone isotopic composition of water in the vapor phase. Wet soils pose particular sampling difficulties, and novel techniques are being developed to address these issues

Uncertainties in the assessment of the isotopic composition of surface fluxes: A direct comparison of techniques using laser-based water vapor isotope analyzers

Author's manuscript made available in accordance with the publisher's policy.The isotopic composition of surface fluxes is a key environmental tracer currently estimated with a variety of methods, including: Keeling mixing models, the flux-gradient technique, and eddy covariance. We present a direct inter-comparison of these three methods used to estimate the isotopic ratio of water vapor in surface fluxes (δET) over half-hour periods, with a focus on the statistical uncertainty of each method image We develop expressions for image a function of instrument precision, sample size, and atmospheric conditions. Uncertainty estimators are validated with high frequency (1 Hz) data from multiple configurations of commercial off-axis integrated cavity output spectroscopy (ICOS) systems. We find measurement techniques utilizing the high frequency capabilities of ICOS system outperform those methods where a single average of the isotopic composition is obtained at each height, with improvements attributed to large sample counts and increased variation in observed concentrations. Analytically, and with supporting data, we show that over 30 minute periods the Keeling plot and flux-gradient techniques produce nearly identicalδET and image values, while eddy covariance calculations always introduce more uncertainty given the same high frequency data. This additional uncertainty is proportional to the reciprocal of the correlation coefficient between vertical wind speed and water vapor mixing ratio. Finally, given the inverse relationship between δET uncertainties and the range of water vapor observed, we propose that experimental designs should attempt to maximize both sample count and the coefficient of variation in atmospheric water vapor

Power-law cosmological solution derived from DGP brane with a brane tachyon field

By studying a tachyon field on the DGP brane model, in order to embed the 4D standard Friedmann equation with a brane tachyon field in 5D bulk, the metric of the 5D spacetime is presented. Then, adopting the inverse square potential of tachyon field, we obtain an expanding universe with power-law on the brane and an exact 5D solution.Comment: 8 pages, 1 figure, accepted by IJMP

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert

Ecosystem-scale spatial heterogeneity of stable isotopes of soil nitrogen in African savannas

Author's manuscript made available in accordance with the publisher's policy.Soil 15N is a natural tracer of nitrogen (N) cycling. Its spatial distribution is a good indicator of processes that are critical to N cycling and of their controlling factors integrated both in time and space. The spatial distribution of soil δ15N and its underlying drivers at sub-kilometer scales are rarely investigated. This study utilizes two sites (dry vs. wet) from a megatransect in southern Africa encompassing locations with similar soil substrate but different rainfall and vegetation, to explore the effects of soil moisture and vegetation distribution on ecosystem-scale patterns of soil δ15N. A 300-m long transect was set up at each site and surface soil samples were randomly collected for analyses of δ15N, %N and nitrate content. At each soil sampling location the presence of grasses, woody plants, Acacia species (potential N fixer) as well as soil moisture levels were recorded. A spatial pattern of soil δ15N existed at the dry site, but not at the wet site. Woody cover distribution determined the soil δ15N spatial pattern at ecosystem-scale; however, the two Acacia species did not contribute to the spatial pattern of soil δ15N. Grass cover was negatively correlated with soil δ15N at both sites owing to the lower foliar δ15N values of grasses. Soil moisture did not play a role in the spatial pattern of soil δ15N at either site. These results suggest that vegetation distribution, directly, and water availability, indirectly, affect the spatial patterns of soil δ15N through their effects on woody plant and grass distributions

Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems

Nonlinear and non-Gaussian processes with constraints are commonly encountered in dynamic estimation problems. Methods for solving such problems either ignore the constraints or rely on crude approximations of the model or probability distributions. Such approximations may reduce the accuracy of the estimates since they often fail to capture the variety of probability distributions encountered in constrained linear and nonlinear dynamic systems. This article describes a practical approach that overcomes these shortcomings via a novel extension of sequential Monte Carlo (SMC) sampling or particle filtering. Inequality constraints are imposed by accept/reject steps in the algorithm. The proposed approach provides samples representing the posterior distribution at each time point, and is shown to satisfy the same theoretical properties as unconstrained SMC. Illustrative examples show that results of the proposed approach are at least as accurate as moving horizon estimation, but computationally more efficient and in addition, the approach indicates the uncertainty associated with these estimates