1,721,176 research outputs found
New Frontiers in Hydrology: Proc. of the 1st CNR – Princeton Workshop on ‘New Frontiers in Hydrology, Princeton, USA, October 23-25, 2003
No full textOn the fractal structure of soil moisture fields
No full textWe study the spatial structure of soil moisture fields within savanna ecosystems, whose persistence is vital because it is the driver of the entire ecological structure and function. These include changes in the physical and biogeochemical conditions of the landscape, affecting vegetation state, soil composition, water fluxes, and solar radiation. We focus on computations of the probabilistic structure of islands of soil moisture, known empirically to be related to that of tree clusters, defined as crossing properties of simulated soil moisture fields. Rainfall is modelled via Cox-Isham space-time fields endowed with characteristic scales. Results show that clusters of soil moisture islands are characterized by robust scale-free structures in the region of a phase transition whose order parameter depends on mean soil moisture. Signatures of this fractal structure are well-defined power laws of size distributions of soil moisture clusters; their perimeters-vs-area relations; variance-vs- area of the fields. These characteristics allow for the estimation of the fractal dimension of the field, and its Hurst coefficient. From the general covariance equation of a fractal field, spatial simulations are possible because its mean and variance are known from the probabilistic structure of soil moisture at a point. Our results identify the statistics of hotspots of microbial activity deduced from proper moisture islands, unattainable otherwise, and thus may guide the design of field and remote observations. The critical order parameter characterizing the phase transition establishes where the fractal structure of soil moisture fields exists as a function of the climatic drivers, and the thresholds reflecting where vegetation survives in the field. An example of application of the phase transition diagram presented here is carried out with reference to the Nylsvley savanna in South Africa
Stochastic description of waterlogging and hydroperiods in wetlands
Full text linkWetlands are found at the interface between aquatic and terrestrial ecosystems, where different hydrologic factors and ecosystem processes interact to generate unique characteristics and a delicate balance between biotic and abiotic factors. The main hydrologic driver of wetland ecosystems is the water level, whose position above or below the ground level, determines the submergence or non-submergence of soil. When the water level lies above the soil surface, soil is saturated and hypoxic conditions affect all biochemical processes, inducing anaerobic microorganism functioning, variation of redox potential, and anoxic stress in plants, that might lead to the death of non-adapted organisms. When the water level is below the soil surface, the soil water balance is similar to that of groundwater-dependent ecosystems, which allows for both oxygen and water supply to the plant roots. Therefore, the succession of the submerged-unsubmerged conditions plays a fundamental role on the ecosystem. Shallow or above-ground water level fluctuations, at the daily time scale, are driven by stochastic precipitation; using a simple process-based model for soil water balance, the dynamics of groundwater level is here described as a function of evapotranspiration, lateral flow to/from an external water body and random precipitation, modeled as a marked Poisson process. This simple model provides the analytical long-term probability distribution of water table depth and the crossing properties of water table dynamics, which are used to study the timing of waterlogging. The interval of time during which a wetland remains flooded, often called "hydroperiod", is represented by the first passage time of water table in down-crossing the soil surface; here we calculate the mean hydroperiod as the Mean First Passage Time of the process, that is a function of the model parameters, and we verify this result with numerical simulations. Focusing on the statistical properties of hydroperiods, we also propose to describe their long term probability distribution with a parametric distribution, whose parameters are linked to the model parameters through simple analytical relations. Numerical simulations again confirm the validity of the approach, and its capability of describing the properties of hydroperiods as a function of the climatic, pedological, and ecological characteristics of wetland
Plants in water-controlled ecosystem: active roles in hydrological processes and response to water stress. I: Scope and general outline
No full textThis series of four papers studies the complex dynamics of water-controlled ecosystems from the hydro-ecological point of view [e.g., I. Rodriguez-Iturbe, Water Resour. Res. 36 (1) (2000) 3-9]. After this general outline, the role of climate, soil, and vegetation is modeled in Part II [F. Laio, A. Porporato, L. Ridolfi, I. Rodriguez-Iturbe, Adv. Water Res. 24 (7) (2001) 707-723] to investigate the probabilistic structure of soil moisture dynamics and the water balance. Particular attention is given to the impact of timing and amount of rainfall, plant physiology, and soil properties. From the statistical characterization of the crossing properties of arbitrary levels of soil moisture, Part III develops an expression for vegetation water stress [A. Porporato, F. Laio, L. Ridolfi, I. Rodriguez-Iturbe, Adv. Water Res. 24 (7) (2001) 725-744]. This measure of stress is then employed to quantify the response of plants to soil moisture deficit as well as to infer plant suitability to given environmental conditions and understand some of the reasons for possible coexistence of different species. Detailed applications of these concepts are developed in Part IV [F. Laio, A. Porporato, C.P. Fernandez-Illescas, I. Rodriguez-Iturbe, Adv. Water Res. 24 (7) (2001) 745-762], where we investigate the dynamics of three different water-controlled ecosystems
Mean first passage times of processes driven by white shot noise
Full text linkThe systems driven by white shot noise are analyzed based on mean first passage times. The shot noise has exponentially distributed jump heights. The the linkage between the results and the steady state probability density function of the process are presented
Intensive or extensive use of soil moisture: plant strategies to cope with stochastic water availability
No full textSome plants rely on a dependable winter recharge, as opposed to others that quickly respond to the intermittent and uncertain rainfall during the growing season. Using a stochastic model for the soil moisture process and a quantitative measure of plant water stress, we find climate, soil, and vegetation characteristics leading to the dominance or possible coexistence of these two strategies of water use
Inferring plant ecosystem organization from species occurrences.
No full textIn this paper, we present an approach capable of extracting insights on ecosystem organization from merely occurrence (presence/absence) data. We extrapolate to the collective behavior by encapsulating some simplifying assumptions within a given set of constraints, and then examine their ecological implications. We show that by using the mean occurrence and co-occurrence of species as constraints, one is able to capture detailed statistics of a plant community distributed across a vast semiarid area of the United States. The approach allows us to quantify the species' effective couplings: Their frequencies exhibit a peak at zero and the minimal pairwise model is able to capture about 80% of the ecosystem structure. Our analysis reveals a relatively stronger impact of the species network on uncommon species and underscores the importance of species pairs experiencing positive couplings. Additionally, we study the associations among species and, interestingly, find that the frequencies of groups of different species, which the approach is able to capture, exhibit a power-law-like distribution
On space-time scaling of cumulated rainfall fields
Full text linkIn the study of space-time rainfall it is particularly important to establish
characteristic properties to guide both theoretical and modeling research efforts. In the
present paper, new observational analyses on the scaling properties of time-evolving
cumulated rainfall fields are presented, and a theoretical framework for their
interpretation is introduced. It is found that the time evolution of the spatial organization
of a cumulated rainfall field produces scaling relationships of spatial variance versus time
and characteristic values for the scaling exponent. The reproduction of these values
constitutes a basic requirement for spatial-temporal field generators in order to model
important properties of real rainfall fields. It is then shown, on theoretical grounds, what
properties the instantaneous rainfall intensity fields must obey in order to reproduce the
experimental observations and how the size of the observation domain affects the scaling
relationships. Some current stochastic models of space-time precipitation are finally
discussed and analyzed in the light of the tools introduced, to show under what
circumstances the models considered give acceptable results. Furthermore, it is shown that
the assumption of an exponential time correlation function, used in many current rainfall
models, is not compatible with observations
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