Stochastic modeling of soil water fluxes in the absence of measured hydraulic parameters requires a knowledge of the expected distribution of the hydraulic parameters in different soil types. Predictive relationships describing the hydraulic parameter distributions must be developed based on the common descriptors of the physical properties of soils (e.g., texture, structure, particle size distribution). Covariation among the hydraulic parameters within these relationships must be identified. Data for 1448 soil samples were examined in an evaluation of the usefulness of qualitative descriptors as predictors of soil hydraulic behavior. Analysis of variance and multiple linear regression techniques were used to derive quantitative expressions for the moments of the hydraulic parameters as functions of the particle size distributions (percent sand, silt, and clay content) of soils. Discriminant analysis suggests that the covariation of the hydraulic parameters can be used to construct a classification scheme based on the hydraulic behavior of soils that is analogous to the textural classification scheme based on the sand, silt, and clay content of soils. INTRODUCTIONApplication of the classical theory of soil water movement requires knowledge of the relationships among matric potential, moisture content, and hydraulic conductivity. The physical attributes of the soil giving rise to these interrelationships are understood in a qualitative sense [e.g., Childs, 1969]. A comprehensive theory to allow derivation of the relationships from fundamental properties of the medium (e.g., grain size distribution) is, however, not yet fully developed, although recent work suggests that certain aspects of the hydraulics may be amenable to a theoretical treatment [Nakano, 1976;Arya and Paris, 1981]. In most cases, curves of matric potential versus moisture content (the moisture characteristic) and of hydraulic conductivity versus either matric potential or moisture content must be determined for a given soil by direct measurement. Statistical analyses can be used to identify what soil properties are important in describing the observed variation in these curves, thereby providing information of practical value as well as suggesting how theoretical exploration might proceed.One approach that has been used to define the moisture characteristic is the construction of regression equations to predict the moisture content at specified values of matric potential using properties such as bulk density, percent sand, and other measured properties such as organic matter content [Ghosh, 1980;Gupta and Larson, 1979;Rawls and Brakensiek, 1982]. Results from these studies indicate that reasonable predictions can be made when the necessary data are available.
Summary Reliable modelling of above‐ground net primary production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. We compared abundance‐weighted values of two of the most widely used traits from the leaf economics spectrum (specific leaf area and leaf dry matter content) with measured aNPP across a temperate ecosystem gradient. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP (R2 = 0·55). Directly measured in situ trait values for the dominant species improved estimation of aNPP significantly. Introducing intraspecific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12832/suppinfo is available for this article.
Loss and fragmentation of natural land cover due to expansion of agricultural areas is a global issue. These changes alter the configuration and composition of the landscape, particularly affecting those ecosystem services (benefits people receive from ecosystems) that depend on interactions between landscape components. Hydrological mitigation describes the bundle of ecosystem services provided by landscape features such as woodland that interrupt the flow of runoff to rivers. These services include sediment retention, nutrient retention and mitigation of overland water flow. The position of woodland in the landscape and the landscape topography are both important for hydrological mitigation. Therefore, it is crucial to consider landscape configuration and flow pathways in a spatially explicit manner when examining the impacts of fragmentation. Here we test the effects of landscape configuration using a large number (>7,000) of virtual landscape configurations. We created virtual landscapes of woodland patches within grassland, superimposed onto real topography and stream networks. Woodland patches were generated with user‐defined combinations of patch number and total woodland area, placed randomly in the landscape. The Ecosystem Service model used hydrological routing to map the “mitigated area” upslope of each woodland patch. We found that more fragmented woodland mitigated a greater proportion of the catchment. Larger woodland area also increased mitigation, however, this increase was nonlinear, with a threshold at 50% coverage, above which there was a decline in service provision. This nonlinearity suggests that the benefit of any additional woodland depends on two factors: the level of fragmentation and the existing area of woodland. Edge density (total edge of patches divided by area of catchment) was the best single metric in predicting mitigated area. Distance from woodland to stream was not a significant predictor of mitigation, suggesting that agri‐environment schemes planting riparian woodland should consider additional controls such as the amount of fragmentation in the landscape. These findings highlight the potential benefits of fragmentation to hydrological mitigation services. However, benefits for hydrological services must be balanced against any negative effects of fragmentation or habitat loss on biodiversity and other services.
Abstract. The catchment scale-experiments of the RAIN and CLIMEX projects conducted on boreal forest ecosystems at Risdalsheia, southernmost Norway, provide a unique set of data on the flux of nitrogen (N) in runoff following changes in N deposition, carbon dioxide (CO2) level and temperature. MERLIN (Model of Ecosystem Retention and Loss of Inorganic Nitrogen), a recently-developed model that focuses on N leaching, provides a means by which these data can be placed into a quantitative framework. The features of the N flux in runoff at Risdalsheia to be explained include (1) leaching of about 30-50 mmol m-2 yr-1 (30-40% of N deposition) during the period 1985-1997 at reference catchments, (2) rapid and dramatic reduction in N leaching following experimental reduction in N deposition in 1985 at KIM catchment, (3) increased flux of about 5 mmol m-2 yr-1 following onset of 3-5°C warming and increased CO2 in 1995 at KIM catchment, and (4) increased flux of about 12 mmol m-2 yr-1 following 3-5°C warming of soil in 1995 at EGIL catchment. One set of calibrated model parameters is sufficient to simulate the changes in N runoff at both experimental catchments for both of the manipulations. The model support the conceptual picture of the soil as the major sink for N inputs from deposition with N accumulating in both the forest floor (labile organic matter LOM) and the bulk soil (refractory organic matter ROM). As the molar carbon/nitrogen (C/N) ratio of LOM decreases to below 23, progressively less N is immobilised and more goes to runoff. The model also supports the conceptual picture of increased rate of decomposition of old soil organic matter in response to higher temperature. An increase of 5% is sufficient to produce the 5-12 mmol m-2 yr-1 increase in N flux in runoff observed at the 2 experimental catchments. The MERLIN simulations are consistent with measurements of increase in net mineralisation rates (per catchment area by 70 mmol m-2 yr-1) and N contents in foliage in treated and reference areas before and after onset of treatment. Runoff provides a very sensitive indicator of changes in N cycling within the ecosystem. Small changes in key processes such as N mineralisation give rise to large relative changes in N flux. Uncertainties in measurements are generally much larger than changes indicated by the model calibration.
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