Interpreting water demands of forests and grasslands within a new Budyko formulation of evapotranspiration using percolation theory.

The relationship between carbon cycle and water demand is key to understanding global climate change, vegetation productivity, and predicting the future of water resources. The water balance, which enumerates the relative fractions of precipitation P that run off, Q, or are returned to the atmosphere through evapotranspiration, ET, links drawdown of atmospheric carbon with the water cycle through plant transpiration. Our theoretical description based on percolation theory proposes that dominant ecosystems tend to maximize drawdown of atmospheric carbon in the process of growth and reproduction, thus providing a link between carbon and water cycles. In this framework, the only parameter is the fractal dimensionality df of the root system. Values of df appear to relate to the relative roles of nutrient and water accessibility. Larger values of df lead to higher ET values. Known ranges of grassland root fractal dimensions predict reasonably the range of ET(P) in such ecosystems as a function of aridity index. Forests with shallower root systems, should be characterized by a smaller df and, therefore, ET that is a smaller fraction of P. The prediction of ET/P using the 3D percolation value of df matches rather closely results deemed typical for forests based on a phenomenology already in common use. We test predictions of Q with P against data and data summaries for sclerophyll forests in southeastern Australia and the southeastern USA. Applying PET data from a nearby site constrains the data from the USA to lie between our ET predictions for 2D and 3D root systems. For the Australian site, equating cited "losses" with PET underpredicts ET. This discrepancy is mostly removed by referring to mapped values of PET in that region. Missing in both cases is local PET variability, more important for reducing data scatter in southeastern Australia, due to the greater relief.

Challenges and advances in measuring sap flow in agriculture and agroforestry: A review with focus on nuclear magnetic resonance.

Sap flow measurement is one of the most effective methods for quantifying plant water use.A better understanding of sap flow dynamics can aid in more efficient water and crop management, particularly under unpredictable rainfall patterns and water scarcity resulting from climate change. In addition to detecting infected plants, sap flow measurement helps select plant species that could better cope with hotter and drier conditions. There exist multiple methods to measure sap flow including heat balance, dyes and radiolabeled tracers. Heat sensor-based techniques are the most popular and commercially available to study plant hydraulics, even though most of them are invasive and associated with multiple kinds of errors. Heat-based methods are prone to errors due to misalignment of probes and wounding, despite all the advances in this technology. Among existing methods for measuring sap flow, nuclear magnetic resonance (NMR) is an appropriate non-invasive approach. However, there are challenges associated with applications of NMR to measure sap flow in trees or field crops, such as producing homogeneous magnetic field, bulkiness and poor portable nature of the instruments, and operational complexity. Nonetheless, various advances have been recently made that allow the manufacture of portable NMR tools for measuring sap flow in plants. The basic concept of the portal NMR tool is based on an external magnetic field to measure the sap flow and hence advances in magnet types and magnet arrangements (e.g., C-type, U-type, and Halbach magnets) are critical components of NMR-based sap flow measuring tools. Developing a non-invasive, portable and inexpensive NMR tool that can be easily used under field conditions would significantly improve our ability to monitor vegetation responses to environmental change.

Optimizing cropping pattern to improve the performance of irrigation network using system dynamics-Powell algorithm.

One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which leads to higher productivity and economic benefits. In this regard, quantitative and qualitative studies of drainage water from networks are essential for efficient water management. In the present study, we develop a model using a system dynamics approach to simulate the cropping pattern of an irrigation and drainage network as well as the discharge and salinity of drainage water from network farms. We apply the Powell algorithm to optimize the economic profitability of cultivated crops by considering the salinity and discharge of drainage water from the fields. With three aims, i.e., (1) maximizing benefit-cost ratio, (2) minimizing drainage water salinity and discharge of network, and (3) economic and environmental considerations simultaneously, the optimization of cropping pattern within the Kosar irrigation and drainage network is performed. Results based on five consecutive years under different scenarios showed that some crops, such as watermelon, are not economically recommened for production due to high costs, water consumption, and low selling price causes environmental pollution. On the other hand, wheat, grain maize, silage maize, sorghum, and alfalfa have different conditions, and their production is suitable by considering all scenarios. By comparing with experimental data, we find that the proposed model is accurate to simulate and optimize the irrigation network and to detect its cropping pattern.

Scale dependence of tortuosity and diffusion: Finite-size scaling analysis.

Physical and hydraulic properties of porous media are routinely measured/simulated at smaller scales e.g., pore and core. However, their determination at larger scales e.g., field and reservoir has still been a great challenge. Although understanding the scale dependence of transport modes in rocks and soils is essential, the porous media community still lacks in a solid theoretic framework. In this short communication, we propose finite-size scaling analysis from physics to investigate the scale dependence of tortuosity and diffusion coefficient. By comparing with two- and three-dimensional simulations, we demonstrate that the finite-size scaling analysis is a powerful approach. More specifically, we show that the plot of simulated tortuosity or diffusion coefficient versus scale looks scattered. However, after applying the finite-size scaling analysis, the data collapse together showing a quasi-universal trend.

Geogenic and anthropogenic sources identification and ecological risk assessment of heavy metals in the urban soil of Yazd, central Iran.

Urban soil pollution with heavy metals is one of the environmental problems in recent years, especially in industrial cities. The aim of this study is to evaluate the role of geogenic and anthropogenic sources in the urban soil pollution in Yazd, Iran. For this purpose, 30 top-soil (0-10 cm) samples from Yazd within an area of 136.37 Km2 and population of nearly 656 thousand are collected, and the concentration of heavy elements is measured. To evaluate factors affecting the concentration of heavy elements in urban soils and determine their possible sources, Multivariate statistical analysis, including correlation coefficient, principal components analysis (PCA) and cluster analysis (CA) are performed. Enrichment Factor (EF), Geo-accumulation index (Igeo), and Modified potential ecological Risk Index (MRI) are used to assess the level and extension of contamination. Results of this study suggest that As, Cd, Pb and Zn are affected by anthropogenic source, while the concentrations of Fe, Mn, Ni, Cr, Co, Cu and Cs have come from mostly natural geologic sources. As, Cd and Pb are considerably enriched in the area, provided moderately enriched for the elements Mn, Zn and Cu. However, the other heavy elements show minimal enrichment. Igeo reveal that Co, Cr, Cs, Cu, Fe, Mn, Zn and Ni with negative values are unpolluted, Pb posed unpolluted to moderately polluted, and As and Cd represent high polluted. Based on the results of the ecological risk factor, the heavy metals of Mn, Ni, Cr, Zn and Cu have a low ecological risk level. More specifically, we find that Pb shows a moderated ecological risk in 39% of the urban soil in the studied area. As and Cd with respectively 100 and 72% contribution have considerable and very high ecological risk. According to the results of MRI, the area is in a very high ecological risk level, and appropriate management practice is essential to reduce the pollution of heavy elements in this area.

Experimental study of hydraulic properties in grain packs: Effects of particle shape and size distribution.

Uniform and multi-dispersed grain packs have been frequently used to conceptually study flow in porous media. Numerical simulations were previously used to address the effect of particle shape on characteristics, such as pore space fractal dimension, moisture characteristic curve (MCC) and saturated hydraulic conductivity (SHC) of grain packs. However, experimental observations are still required since fractal-based approaches have been extensively proposed to model various properties in porous media. In this study, 16 angular sand and 16 spherical glass bead samples with different particle size distributions (PSDs) from well- to poorly-sorted were packed. The MCC was measured using the combination of sandbox and pressure plates methods. The pore space fractal dimension (DMCC), calculated from the measured MCC, ranged from 0.80 to 2.86 in sand and from -0.18 to 2.81 in glass bead packs, which indicated that DMCC may be negative in homogeneous media (e.g., glass bead packs) consistent with several studies in the literature. Results showed greater DMCC for the sand packs than the glass bead packs with the same geometric mean diameter values and PSDs. This clearly demonstrated the effect of particle shape on DMCC in the studied packs. The critical path analysis (CPA) approach was used to estimate the SHC measured using the constant-head method. We found that the CPA estimated the SHC accurately, within a factor of four of the measurements on average. Although the CPA is theoretically known to be accurate in media with broad pore size distributions, we experimentally found that it estimated the SHC in various types of grain packs reasonably well.

Predicting Water Cycle Characteristics from Percolation Theory and Observational Data.

The fate of water and water-soluble toxic wastes in the subsurface is of high importance for many scientific and practical applications. Although solute transport is proportional to water flow rates, theoretical and experimental studies show that heavy-tailed (power-law) solute transport distribution can cause chemical transport retardation, prolonging clean-up time-scales greatly. However, no consensus exists as to the physical basis of such transport laws. In percolation theory, the scaling behavior of such transport rarely relates to specific medium characteristics, but strongly to the dimensionality of the connectivity of the flow paths (for example, two- or three-dimensional, as in fractured-porous media or heterogeneous sediments), as well as to the saturation characteristics (i.e., wetting, drying, and entrapped air). In accordance with the proposed relevance of percolation models of solute transport to environmental clean-up, these predictions also prove relevant to transport-limited chemical weathering and soil formation, where the heavy-tailed distributions slow chemical weathering over time. The predictions of percolation theory have been tested in laboratory and field experiments on reactive solute transport, chemical weathering, and soil formation and found accurate. Recently, this theoretical framework has also been applied to the water partitioning at the Earth's surface between evapotranspiration, ET, and run-off, Q, known as the water balance. A well-known phenomenological model by Budyko addressed the relationship between the ratio of the actual evapotranspiration (ET) and precipitation, ET/P, versus the aridity index, ET0/P, with P being the precipitation and ET0 being the potential evapotranspiration. Existing work was able to predict the global fractions of P represented by Q and ET through an optimization of plant productivity, in which downward water fluxes affect soil depth, and upward fluxes plant growth. In the present work, based likewise on the concepts of percolation theory, we extend Budyko's model, and address the partitioning of run-off Q into its surface and subsurface components, as well as the contribution of interception to ET. Using various published data sources on the magnitudes of interception and information regarding the partitioning of Q, we address the variability in ET resulting from these processes. The global success of this prediction demonstrated here provides additional support for the universal applicability of percolation theory for solute transport as well as guidance in predicting the component of subsurface run-off, important for predicting natural flow rates through contaminated aquifers.