Spatial analysis of storm depths from an Arizona raingage network

Eight years of summer rainstorm observations are analyzed by a dense network of 93 raingages operated by the U.S. Department of Agriculture, Agricultural Research Service, in the 150 km Walnut Gulch experimental catchment near Tucson, Arizona. Storms are defined by the total depths collected at each raingage during the noon-to-noon period for which there was depth recorded at any of the gages. For each of the resulting 428 storm days, the gage depths are interpolated onto a dense grid and the resulting random field analyzed to obtain moments, isohyetal plots, spatial correlation function, variance function, and the spatial distribution of storm depth

Space-time modeling of soil moisture: Stochastic rainfall forcing with heterogeneous vegetation

The present paper complements that of Isham et al. (2005), who introduced a space-time soil moisture model driven by stochastic space-time rainfall forcing with homogeneous vegetation and in the absence of topographical landscape effects. However, the spatial variability of vegetation may significantly modify the soil moisture dynamics with important implications for hydrological modeling. In the present paper, vegetation heterogeneity is incorporated through a two dimensional Poisson process representing the coexistence of two functionally different types of plants (e.g., trees and grasses). The space-time statistical structure of relative soil moisture is characterized through its covariance function which depends on soil, vegetation, and rainfall patterns. The statistical properties of the soil moisture process averaged in space and time are also investigated. These properties are especially important for any modeling that aggregates soil moisture characteristics over a range of spatial and temporal scales. It is found that particularly at small scales, vegetation heterogeneity has a significant impact on the averaged process as compared with the uniform vegetation case. Also, averaging in space considerably smoothes the soil moisture process, but in contrast, averaging in time up to 1 week leads to little change in the variance of the averaged process

Spatial characteristics of observed precipitation fields: A catalog of summer storms in Arizona, Volume 2

The parameters of the conceptual model are evaluated from the analysis of eight years of summer rainstorm data from the dense raingage network in the Walnut Gulch catchment near Tucson, Arizona. The occurrence of measurable rain at any one of the 93 gages during a noon to noon day defined a storm. The total rainfall at each of the gages during a storm day constituted the data set for a single storm. The data are interpolated onto a fine grid and analyzed to obtain: an isohyetal plot at 2 mm intervals, the first three moments of point storm depth, the spatial correlation function, the spatial variance function, and the spatial distribution of the total storm depth. The description of the data analysis and the computer programs necessary to read the associated data tapes are presented

A stronger topology for the Brownian web

We propose a metric space of coalescing pairs of paths on which we are able to prove (more or less) directly convergence of objects such as the persistence probability in the (one dimensional, nearest neighbor, symmetric) voter model or the diffusively rescaled weight distribution in a silo model (as well as the equivalent output distribution in a river basin model), interpreted in terms of (dual) diffusively rescaled coalescing random walks, to corresponding objects defined in terms of the Brownian web.Comment: 22 page

Cellular Models for River Networks

A cellular model introduced for the evolution of the fluvial landscape is revisited using extensive numerical and scaling analyses. The basic network shapes and their recurrence especially in the aggregation structure are then addressed. The roles of boundary and initial conditions are carefully analyzed as well as the key effect of quenched disorder embedded in random pinning of the landscape surface. It is found that the above features strongly affect the scaling behavior of key morphological quantities. In particular, we conclude that randomly pinned regions (whose structural disorder bears much physical meaning mimicking uneven landscape-forming rainfall events, geological diversity or heterogeneity in surficial properties like vegetation, soil cover or type) play a key role for the robust emergence of aggregation patterns bearing much resemblance to real river networks.Comment: 7 pages, revtex style, 14 figure

Stochastic description of waterlogging and hydroperiods in wetlands

Wetlands 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 wetlands

Models of Soil Moisture Dynamics in Ecohydrology: A Comparative Study

An accurate description of plant ecology requires an understanding of the interplay between precipitation, infiltration, and evapotranspiration. A simple model for soil moisture dynamics, which does not resolve spatial variations in saturation, facilitates analytical expressions of soil and plant behavior as functions of climate, soil, and vegetation characteristics. Proper application of such a model requires knowledge of the conditions under which the underlying simplifications are appropriate. To address this issue, we compare predictions of evapotranspiration and root zone saturation over a growing season from a simple bucket-filling model to those from a more complex, vertically resolved model. Dimensionless groups of key parameters measure the quality of the match between the models. For a climate, soil, and woody plant characteristic of an African savanna the predictions of the two models are quite similar if the plant can extract water from locally wet regions to make up for roots in dry portions of the soil column; if not, the match is poor

Evaluating ecohydrological modelling framework to link atmospheric CO2 and stomatal conductance

The establishment of an accurate stomatal conductance (gs) model in responding to CO2 enrichment under diverse environmental conditions remains an important issue as gs is the key to understand the plant–water–atmosphere interactions. A better representation of gs is important to reduce uncertainties in predicting the climate change impacts on various ecosystem functions. In this study, we evaluated three most commonly used gs formulations for the estimation of the stomatal response to environmental factors using in situ measurements under different environmental conditions. The three gs models were Leuning's modified Ball–Berry model and two specific cases of the optimization models (i.e., Rubisco limitation model and RuBP regeneration limitation model). On the basis of an analysis of 234 data points obtained from experiments under instantaneous, semicontrolled, and the free‐air CO2 experiment conditions, we found that Leuning's modified Ball–Berry model and RuBP‐limited optimization model showed similar performance, and both performed better than Rubisco limitation model. Functional groups (e.g., C3 vs. C4 species) and life form (e.g., annual vs. perennial species) play an important role in determining the gs model performance and thus pose a challenge for gs predictions in mixed vegetation communities