Medium- and Long-term Recovery of Estuarine and Coastal Ecosystems: Patterns,Rates and Restoration Effectiveness

Many estuarine and coastal marine ecosystems have increasingly experienced degradation caused by multiple stressors. Anthropogenic pressures alter natural ecosystems and the ecosystems are not considered to have recovered unless secondary succession has returned the ecosystem to the pre-existing condition or state. However, depending upon the scales of time, space and intensity of anthropogenic disturbance, return along the historic trajectory of the ecosystem may: (1) follow natural restoration though secondary succession; (2) be re-directed through ecological restoration, or (3) be unattainable. In order to address the gaps in knowledge about restoration and recovery of estuarine and coastal ecosystems, this special feature includes the present overview and other contributions to provide a synthesis of our knowledge about recovery patterns, rates and restoration effectiveness. From the 51 examples collated in this contribution, we refine the recovery from the list of stressors into six recovery mechanisms: (1) recovery from sediment modification, which includes all aspects of dredging and disposal; (2) recovery by complete removal of stressors limiting natural ecosystem processes, which includes tidal marsh and inundation restoration; (3) recovery by speed of organic degradation, which includes oil discharge, fish farm wastes, sewage disposal, and paper mill waste; (4) recovery from persistent pollutants, which includes chemical discharges, such as TBT; (5) recovery from excessive biological removal, related to fisheries and (6) recovery from hydrological and morphological modification. Drawing upon experience both from these many examples and from an example of one comprehensive study, we show that although in some cases recovery can take <5 years, especially for the short-lived and high-turnover biological components, full recovery of coastal marine and estuarine ecosystems from over a century of degradation can take a minimum of 15–25 years for attainment of the original biotic composition and diversity may lag far beyond that period.     

aiNet- and GIS-based regional prediction system for the spatial and temporal probability of rainfall-triggered landslides

We developed a real-time forecasting system, aiNet-GISPSRIL, for evaluating the spatiotemporal probability of occurrence of rainfall-triggered landslides. In this system, the aiNet (a kind of artificial neutral network based on a self-organizing system) and GIS are merged for integrating the rainfall conditions into various environmental factors that influence the landslide occurrence and for simulating the complex non-linear relationships between landslide occurrence and its related conditions. Zhejiang Province (101,800 km² in area), located in the southeast coastal region of China, is highly prone to the occurrence of landslides during intensive rainfall. Since 2003, the aiNet-GISPSRIL has been used to predict landslides during the rainy seasons in the region. The aiNet-GISPSRIL uses the regional 24-h forecast rainfall information and the real-time rainfall monitoring data from the rain-gauge network as its inputs, and then provides 24-h forecast of the landslide probability for every 1 × 1-km grid cell within the region. Verification studies on the performance of the aiNet-GISPSRIL show that the system has successfully predicted the dates and localities of 304 landslides (accounting for 66.2% of reported landslides during the period). During the period from 2003 to 2007, because the system provided the probability levels of landslide occurrences up to 24-h in advance, gave locations of potential landslides, and timely warned those individuals at high-risk areas, more than 1700 persons living in the risk sites had been evacuated to safe ground before the landslides occurred and thus casualty was avoided. This highly computerized, easy-operating system can be used as a prototype for developing forecasting systems in other regions that are prone to rainfall-triggered landslides.     

Scattered Data Approximation on the Bisphere and Application to Texture Analysis

The paper deals with the approximation and optimal interpolation of functions defined on the bisphere \mathbb S²×\mathbb S²\mathbb {S}^{2}\times \mathbb {S}^{2} from scattered data. We demonstrate how the least square approximation to the function can be computed in a stable and efficient manner. The analysis of this problem is based on Marcinkiewicz–Zygmund inequalities for scattered data which we present here for the bisphere. The complementary problem of optimal interpolation is also solved by using well-localized kernels for our setting. Finally, we discuss the application of the developed methods to problems of texture analysis in material science.     

Hydrodynamic conditions and karst formation in the northwest part of Syria

The study of hydrodynamic zonation and karst formation in the northwest part of Syria is a topical hydrogeological problem. It is important to trace the history of karst formation and evaluate the spread of karst structures in the area and the rate of development of karst processes, as well as their influence on water supplies and irrigation. The solution of this problem was performed by integrated geophysical methods (electric and seismic exploration works) and data analysis of well-drilling, geological, hydro-geological, and subsurface territory explorations. The most detailed works were done near Ras Al-Ain. This paper is devoted to the results of the study of hydrodynamic conditions and karst formation.     

Macrozooplankton Community Dynamics in Relation to Environmental Variables in Willapa Bay,Washington, USA

Willapa Bay is a large, economically and ecologically important estuary on the Washington coast, USA for which the zooplankton community has not previously been studied. Thus, in 2006 and 2007, six stations within Willapa Bay were sampled biweekly for macrozooplankton, chlorophyll, and various abiotic variables to elucidate the processes underlying community composition and dynamics. Non-metric multidimensional scaling identified water temperature and upwelling values as major factors defining two distinct temporal communities. High densities and a community dominated by oceanic species (Calanus pacificus, Centropages abdominalis) marked the winter season, while summer (or the upwelling season) was dominated by estuarine species (Palaemonidae, Clevelandia ios). Smaller scale changes in the community were characterized by variation in chlorophyll a concentration and salinity and were marked by the presence of other taxa (Neotrypaea californiensis, Mysidae). These results point to the importance of physical processes, including the import of marine organisms and retention of estuarine organisms, in the structuring of the macrozooplankton community in Willapa Bay.     

Assessment of NO<Subscript>3</Subscript><Superscript>−</Superscript> contamination in a karstic aquifer,with the use of geochemical data and spatial analysis

Kopaida plain is a cultivated region of Eastern Greece, with specific characteristic related with the paleogeographic evolution and the changes in land use. The present article examines the contamination that derives from nitrates, in terms of contaminant levels, definition of sources and spatial distribution of contaminant plume. For this purpose, 50 water samples were collected from the karstic aquifer and analyzed for 15 parameters including major ions, trace elements, physicochemical parameters, and stable isotopes. The assessment of the above parameter values along with the notes derived by the statistical process revealed the existence of nitrate contamination which has been spatial defined with the aid of spatial interpolation techniques. The correlation of NO₃ ⁻ concentrations with the stable isotope values, defined the infiltration conditions and showed contaminant transport. Nitrate values revealed the potential environmental threat for local people, as 10% of the samples exceeded the parametric value of 50 ppm and 54% of them are above 25 ppm, indicating no optimal quality conditions. The origin of nitrate contamination seems to derive exclusively from the application of N-fertilizers, since the rest of potential sources were not verified by analytical data and field works.     

2D stabilised analytic signal method in DC pole-pole potential data interpretation

Using analytic signal method, interpretation of pole-pole secondary electric potentials due to 2D conductive/resistive prisms is presented. The estimated parameters are the location, lateral extent or width and depth to top surface of the prism. Forward modelling is attempted by 2D-Finite Difference method. The proposed stabilised analytic signal algorithm (RES2AS) uses Tikhonov’s regularization scheme and FFT routines. The algorithm is tested on three theoretical examples and field data from the campus of Roorkee University. The stability of RES2AS is also tested on synthetic error prone secondary pole-pole potential data.     

Ensemble filter methods with perturbed observations applied to nonlinear problems

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.     

Impacts of systematic precipitation bias on simulations of water and energy balances in Northwest America

At high latitudes and in mountainous areas, evaluation and validation of water and energy flux simu-lations are greatly affected by systematic precipitation errors. These errors mainly come from topographic effects and undercatch of precipitation gauges. In this study, the Land Dynamics （LAD） land surface model is used to investigate impacts of systematic precipitation bias from topography and wind-blowing on water and energy flux simulation in Northwest America. The results show that topographic and wind adjustment reduced bias of streamflow simulations when compared with observed streamflow at 14 basins. These systematic biases resulted in a -50%-100% bias for runoff simulations, a -20%-20% bias for evapotranspiration, and a -40%-40% bias for sensible heat flux, subject to different locations and adjustments, when compared with the control run. Uncertain gauge adjustment leads to a 25% uncertainty for precipitation, a 20% 100% uncertainty for runoff simulation, a less-than-10% uncertainty for evapotranspiration, and a less-than-20% uncertainty for sensible heat flux.     

Sand eddies induced by cyclic tilt of a retaining wall

The cyclic tilt of a retaining wall induces a peculiar motion in the backfill (sand), which exhibits closed trajectories (eddies). In this paper, the motion of the backfill has been optically traced and analyzed by means of particle image velocimetry, also known as digital image correlation. The results are of importance for cyclically loaded structures (e.g, piles for off-shore structures) and can also serve to test numerical simulations of large deformation.