A network theory approach for a better understanding of overland flow connectivity

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub‐) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub‐Mediterranean environment by using a combination of high‐resolution digital‐terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi‐natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Retaining wall optimization using interior search algorithm with different bound constraint handling

Along with the applicability of optimization algorithms, there are lots of features that can affect the functioning of the optimization techniques. The main purpose of this paper is investigating the significance of boundary constraint handling (BCH) schemes on the performance of optimization algorithms. To this end, numbers of deterministic and probabilistic BCH approaches are applied to one of the most recent proposed optimization techniques, named interior search algorithm (ISA). Apart from the implementing different BCH methods, a sensitivity analysis is conducted to find an appropriate setting for the only parameter of ISA. Concrete cantilever retaining wall design as one of the most important geotechnical problems is tackled to declare proficiency of the ISA algorithm, on the one hand, and benchmark the effect of BCH schemes on the final results, on the contrary. As results demonstrate, various BCH approaches have a perceptible impact on the algorithm performance. In like manner, the essential parameter of ISA can also play a pivotal role in this algorithm's efficiency. Copyright © 2017 John Wiley & Sons, Ltd.     

Pre‐ and post‐impoundment nitrogen in the lower Missouri River

Large water‐sample sets collected from 1899 through 1902, 1907, and in the early 1950s allow comparisons of pre‐impoundment and post‐impoundment (1969 through 2008) nitrogen concentrations in the lower Missouri River. Although urban wastes were not large enough to detectably increase annual loads of total nitrogen at the beginning of the 20^th century, carcass waste, stock‐yard manure, and untreated human wastes measurably increased ammonia and organic‐nitrogen concentrations during low flows. Average total‐nitrogen concentrations in both periods were about 2.5 mg/l, but much of the particulate‐organic nitrogen, which was the dominant form of nitrogen around 1900, has been replaced by nitrate. This change in speciation was caused by the nearly 80% decrease in suspended‐sediment concentrations that occurred after impoundment, modern agriculture, drainage of riparian wetlands, and sewage treatment. Nevertheless, bioavailable nitrogen has not been low enough to limit primary production in the Missouri River since the beginning of the 20^th century. Nitrate concentrations have increased more rapidly from 2000 through 2008 (5 to 12% per year), thus increasing bioavailable nitrogen delivered to the Mississippi River and affecting Gulf Coast hypoxia. The increase in nitrate concentrations with distance downstream is much greater during the post‐impoundment period. If strategies to decrease total‐nitrogen loads focus on particulate N, substantial decreases will be difficult because particulate nitrogen is now only 23% of total nitrogen in the Missouri River. A strategy aimed at decreasing particulates also could further exacerbate land loss along the Gulf of Mexico, which has been sediment starved since Missouri River impoundment. In contrast, strategies or benchmarks aimed at decreasing nitrate loads could substantially decrease nitrogen loadings because nitrates now constitute over half of the Missouri's nitrogen input to the Mississippi. Ongoing restoration and creation of wetlands along the Missouri River could be part of such a nitrate‐reduction strategy. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.     

A middle–late Ediacaran volcano‐sedimentary record from the eastern Arabian‐Nubian shield

The Ediacaran Jibalah Group comprises volcano‐sedimentary successions that filled small fault‐bound basins along the NW–SE‐trending Najd fault system in the eastern Arabian‐Nubian Shield. Like several other Jibalah basins, the Antaq basin contains exquisitely preserved sedimentary structures and felsic tuffs, and hence is an excellent candidate for calibrating late Ediacaran Earth history. Shallow‐marine strata from the upper Jibalah Group (Muraykhah Formation) contain a diversity of load structures and intimately related textured organic (microbial) surfaces, along with a fragment of a structure closely resembling an Ediacaran frond fossil and a possible specimen of Aspidella. Interspersed carbonate beds through the Muraykhah Formation record a positive δ¹³C shift from ?6 to 0‰. U‐Pb zircon geochronology indicates a maximum depositional age of ~570 Ma for the upper Jibalah Group, consistent with previous age estimates. Although this age overlaps with that of the upper Huqf Supergroup in nearby Oman, these sequences were deposited in contrasting tectonic settings on opposite sides of the final suture of the East African Orogen.