Prediction Of Future Dynamics Research Articles

Hydrogeological modelling of Olkaria domes geothermal field to predict ground subsidence

Ground subsidence studies have been done on Olkaria geothermal field conventionally by comparing levels on benchmarks over years. Interferometric synthetic aperture radar (InSAR) systems have also been used to map surface deformation of small spatial extent. For the prediction of future dynamics of land subsidence in Olkaria due to geothermal resource exploitation, a hydrogeological conceptual model has been developed. In this model, hydrologic geothermal fluid properties are analysed and a relationship between the reservoir and geology of the wells established, subsidence is computed numerically. The model takes into account the hydrogeological condition of Olkaria geothermal field. Hydrological reservoir parameters are computed from well testing data. The study considers the Injectivity indices of the various wells under study as pre-computational indicator of the expected subsidence extents. Both two- and three-dimensional geological cross-sections are modelled with the rockworks software by inputting stratigraphic data for Olkaria domes. Geological simulations are used to study subsidence by assigning the ground formation with virtual material that deformed according to some essential relations in Rockworks computer software. Production zones are determined by a comparison between the well properties and corresponding well geology. Subsidence is then computed by the Tezarghi’s modified equation. Cumulative subsidence figures from the computation are in the range of 0.095-0.537m, without any reinjection. Computed values are then mapped in ArcGIS to develop a representative subsidence map. By application of these modelling and numerical computation methods, ground subsidence was effectively predicted using the five selected wells in Olkaria domes field. The hydrogeological model developed, and mapping is an important tool in the planning and development of a reinjection schedule and in subsidence mitigation. Subsidence prediction also is important in design of infrastructure which will be strong enough to resist the forces caused by subsidence.

Deep desiccation of soils observed by long-term high-resolution measurements on a large inclined lysimeter

Abstract. Availability of long-term and high-resolution measurements of soil moisture is crucial when it comes to understanding all sorts of changes to past soil moisture variations and the prediction of future dynamics. This is particularly true in a world struggling against climate change and its impacts on ecology and the economy. Feedback mechanisms between soil moisture dynamics and meteorological influences are key factors when it comes to understanding the occurrence of drought events. We used long-term high-resolution measurements of soil moisture on a large inclined lysimeter at a test site near Karlsruhe, Germany. The measurements indicate (i) a seasonal evaporation depth of over 2 m. Statistical analysis and linear regressions indicate (ii) a significant decrease in soil moisture levels over the past 2 decades. This decrease is most pronounced at the start and the end of the vegetation period. Furthermore, Bayesian change-point detection revealed (iii) that this decrease is not uniformly distributed over the complete observation period. The largest changes occur at tipping points during years of extreme drought, with significant changes to the subsequent soil moisture levels. This change affects not only the overall trend in soil moisture, but also the seasonal dynamics. A comparison to modeled data showed (iv) that the occurrence of deep desiccation is not merely dependent on the properties of the soil but is spatially heterogeneous. The study highlights the importance of soil moisture measurements for the understanding of moisture fluxes in the vadose zone.

Daily minimum temperature and precipitation control on spring phenology in arid‐mountain ecosystems in China

AbstractClimate anomaly has caused substantial shifts in phenology of mountain ecosystems, but the underlying mechanism of phenological responses to climate change is still not well understood. In essence, the abundance of vegetation communities increases the complexity of phenology‐climate relationships, leading to a certain limitation in predictions of future dynamics among different vegetation types using a unified model. In this study, we focused on the climatic constraints on spring phenology in arid mountains (AMs) of China, and emphasis was laid on accurate representation of mechanisms that control phenology across different vegetation types. We Firstly explored spatio‐temporal variations in satellite‐derived estimates of starting date of vegetation growing season (SOS) over the period 2000–2015 using moderate‐resolution imaging spectro‐radiometer (MODIS) normalized difference vegetation index (NDVI). Phenological models in response to climate variability were then established by using mixed‐effect models based on satellite observations and an extensive dataset of climatic measurements. Our results showed that the climatic regulation on SOS varied greatly over vegetation types. More climatic factors that regulate phenological development were found in grassland than forest and shrubland. At ecosystem level, two critical climate factors, daily minimum temperature (Tmin) and precipitation, explained 74–95% of total variability in predicted SOS. The observed sensitivity to Tmin is expected to be closely linked with the risk of frost damage, while preseason precipitation determines water availability in spring. The varying ecosystem sensitivity revealed the different resilience and adaptability to changing climate among vegetation types, which have been linked to their eco‐physiological characteristics (e.g., water use efficiency) and environmental conditions (e.g., elevation). Overall, our results indicate a strong dependence of spring phenology on Tmin and precipitation, and create an opportunity for a more realistic representation of vegetation phenology and growth of AM plants in China in land surface models.

Two stage approach to functional network reconstruction for binary time-series

The deduction of network connectivity from the observed node dynamics is costly in large networks. The theoretical number of possible networks containing N nodes connected by binary links grows exponentially with N square. This problem is often termed “the curse of dimensionality”. In practice, unfeasible long time-series and a high computational cost are required to detect the connectivity of a network from its observations. Given the large number of time-series currently assembled in all domains of science, a solution to this inverse problem in large networks is required. We here propose a solution to the inverse problem in large networks of binary variables through a redefinition of the problem. Instead of attempting to deduce the links of a network, we redefine the problem into the prediction of future dynamics. Specifically, we show that links between nodes can be divided into links affecting the future dynamics and links that do not. We further show that hard-to-predict links belong to the second group, and as such can be ignored when predicting future dynamics. This division is applied through a two stage algorithm. In the first stage, the vast majority of potential links (pairs of nodes) is removed, since even if they exist they do not affect the dynamics. At the second stage, a rapid high-precision estimate of the predictable links is performed using a modified partial correlation algorithm. A good predictor for the classification of potential links is the mutual information between a node-pair. Similarly, some nodes have practically no variability and as such have practically no effect on the dynamics of other nodes. The links to and from such nodes are hard to predict. We show that a two stage algorithm can be applied to these nodes with similar results. This methodology does not reproduce the network that originally induced the dynamics, but its prediction of future dynamics is similar to the one of the real network. The current analysis is limited to reconstruction using partial correlation methods. However, the same principle can be applied to other reconstruction methods.

Joining of the historical research and future prediction as a support tool for the assessment of management strategy for European beech-dominated forests in protected areas

European beech-dominated forests are crucial for maintaining biodiversity in forested mountain landscapes of the European temperate zone. This paper presents the results of research and assessment of management strategy for mountain beech-dominated forests in the Jeseníky Mountains (Czech Republic). Our approach is based on combining research on historical development of the forest ecosystem, assessment of its current state, and predictions of future dynamics using a forest growth simulation model. Using such a method makes it possible to understand the current state of the mountain beech-dominated forest ecosystem and predict its future development as a response to specific management strategies. The application of this method is therefore appropriate for assessing the suitability of selected management strategies in mountain protected areas. Our results show that a non-intervention management for mountain beech forest in the next 80 years complies with the Natura 2000 requirement to maintain the existing character of the forest habitat. Thus, the current management plan for the beech-dominated forests in the Jeseníky Mountains does not require significant corrections in the context of its conservation targets (i.e. maintaining biodiversity and current character of the forest ecosystem dominated by beech). The results of this study suggest that combining the knowledge on historical development with forest growth simulation can be used as a suitable support tool to assess management strategies for forest habitats in protected areas.

Assessment of Forest Management in Protected Areas Based on Multidisciplinary Research

The remnants of primeval Norway spruce forests in the European temperate zone are crucial for maintaining forest biodiversity in high mountain landscapes. This paper presents results of a multidisciplinary research and evaluation project on the management practices for mountain spruce forests in the Natura 2000 site (National Nature Reserve Serak-Keprnik in the Hruby Jesenik Mountains, the Czech Republic). Results are based on combining research on the historical development of the forest ecosystem and predictions of future dynamics using a forest growth simulation model. The presented results show that a non-intervention management strategy for mountain spruce forest in the next 50 years complies with the Natura 2000 requirement to maintain the existing character of the forest habitat. Thus, the results indicate that the current management plan for the spruce forests does not require significant corrections in the context of its conservation goals (i.e., maintaining biodiversity and current character of the forest ecosystem dominated by Norway spruce). The results of this study suggest that combining the knowledge of historical development with forest inventory data using forest growth simulation represents a suitable support tool for the assessment of management practices for forest habitats in protected areas.

Quantum Mechanics is not Physical Reality But Mental Potentiality Because of the Law of Non-Contradiction

Quantum mechanics have always shown great predictive successes, but also some weird aspects concerning the mathematics-reality correspondence, such as superposition of contradictory events, like a dead and alive cat. Although physicists claim that we have to live with these contradictions, a psycho-biological analysis could propose another explanation. Observation of extra-mental reality is based on direct physical contact between an object, its corresponding sense organ and its mental representation in the brain. In contrast, prediction of the future is no longer directly linked to extra-mental reality, but projects possible observations from the memory of the past into the future. Due to the uncertainty of the future, predictions require mental potentiality, meaning that it may or may not happen in extra-mental reality. Nevertheless, if past observations are regular as in classical physics, they allow predictions with high reliability, whereas if they are irregular as in quantum physics, they are limited to uncertainty and probability. Superposition in classical physics increases space or time units accordingly, whereas quantum superposition considers multiple space locations for the same object at the same time. Thus, the quantum mechanical formalism is in direct contradiction to the philosophical law of non-contradiction, which does not allow considering it as extra-mental reality. However, it has all characteristics of mental potentiality, which allows prediction of future outcomes with probability. The consideration of quantum mechanics as mental potentiality would solve the superposition problem, as well as the measurement and the non-locality problem. According to the regularity or irregularity of observation, classical or quantum mechanical formalism has to be applied for prediction of future dynamics. There is no collapse or continuation of superposition in the wave function, but simply the replacement of an uncertain prediction model by the more certain observation. With this interpretation some weird aspects could be completely eliminated.

Evolution in ecological field experiments: implications for effect size

Rapid evolution in response to strong selection, much of which is human-induced, has been indisputably documented. In this perspective, we suggest that adaptation may influence the effect size of treatments in ecological field experiments and alter our predictions of future dynamics in ecological systems. Field experiments often impose very strong and consistent selection over multiple generations. Focal populations may adapt to these treatments and, in the process, increase or decrease the magnitude of the treatment effect through time. We argue that how effect size changes through time will depend on the evolutionary history of the experimental population, the type of experimental manipulation, and the traits involved in adaptive responses. While no field study has conclusively demonstrated evolution in response to treatments with concomitant changes in ecological effect size, we present several examples that provide strong circumstantial evidence that such effects occur. We conclude with a consideration of the differences between plastic and genetic responses to treatments and discuss future research directions linking adaptation to ecological effect size.

Late Quaternary linear dune accumulation and chronostratigraphy of the southwestern Kalahari: implications for aeolian palaeoclimatic reconstructions and predictions of future dynamics

The linear dunes of the Kalahari, now largely inactive, have long been identified as having potential palaeoenvironmental significance. The application of optically stimulated luminescence (OSL) dating to these dunes in the 1990s provided the first chronology of aeolian accumulation in this region, though field methodologies and time-consuming multiple-aliquot laboratory protocols limited both the depth of sampling in dune bodies and the total number of samples dated. In order to permit a more thorough investigation of the potential of these dunes to preserve long chronological records, this intensive study presents 71 OSL ages from the linear dunes of the southwestern Kalahari at Witpan, South Africa, sampled with coring equipment at regular and frequent intervals down to bedrock. The earliest sand accumulation recorded at Witpan is at 104 ka, and in spatially discrete locations, other evidence of dune activity is recorded at 77–76, 57–52 and 35–27 ka. Although an inherently discontinuous archive, the linear dunes of the southwestern Kalahari have the potential to record multiple phases of dune construction. Following the Last Glacial Maximum there is near continuous evidence of dune-building, with a peak of accumulation recorded from 15 to 9 ka at five individual sites. This latter period is generally recognised from other proxy evidence as being unusually arid in this region, and such periods of dune activity are likely to be related to intensification of the continental anticyclone. During the Holocene, accumulation has continued at most sites sampled, albeit at a lesser intensity. This may imply that these dunes are presently not far from thresholds of activation.

Spatial and temporal dynamics in canopy dominance of an old-growth central Appalachian forest

Many old-growth deciduous forests in eastern North America exhibit dynamics that suggest succession from dominance by oaks (Quercus spp.) to dominance by maples (Acer spp.). We examined this phenomenon using 20 years of vegetation data from an old-growth central Appalachian forest. Considering the site as a whole, the importance of Quercus spp. declined and that of Acer spp. increased. In particular, Acer rubrum L. exhibited increasing importance in the overstory canopy on upper slopes, and Quercus spp. exhibited a contraction in spatial distribution, particularly in the midstory (P < 0.05). This narrowing of distribution in Quercus spp. was associated with regeneration being restricted to dry, acidic sites. We also considered successional dynamics within three previously identified ecological communities in the study area: oak, mixed mesophytic, and beech. This analysis indicated that increased A. rubrum importance was limited to the upper-slope oak community. A successional dynamic was not apparent in the mixed mesophytic community, and the beech community was characterized by increasing importance of Tsuga canadensis (L.) Carr. Our results suggest that ecological communities have distinct successional trajectories and that predictions of future dynamics must consider topographic and ecological gradients.