1,721,067 research outputs found
Functional comparisons between unimodal and bimodal analytical relationships in terms of water balance predictions for the case study of the Vesuvius volcanic area (Naples, Southern Italy).
No full textOptimal performance of large-scale numerical modeling of the soil-vegetation-atmosphere (SVA) system mandates
accurate assessment and description of the soil hydraulic properties, namely the water retention (WRF) and
hydraulic conductivity (HCF) functions. These functions are commonly described by simple unimodal analytical
relations that guarantee mathematical flexibility with few parameters in the majority of soil types. However, other
soils, like volcanic soils, are characterized by a complex structure yielding a bimodal or even a multimodal distribution
of pore sizes. In these cases, reliable hydrologic predictions can be obtained resorting to more complex
hydraulic functions, yet more accurate and robust ones. To overcome some drawbacks of the classic unimodal hydraulic
relationships, Romano et al. (2011) have developed closed-form bimodal lognormal relations for improving
the description of both WRF and HCF. However, the reliability of this description of the soil hydraulic behavior is
often tested at the curve fitting level only. Comparisons between unimodal and bimodal soil hydraulic relationships
are more effective and informative when performed in functional terms. Therefore, as the primary objective of this
study, we used a hydrological balance model to quantify and compare soil moisture flow and storage regimes for 14
years (1999-2012), when characterized by unimodal or bimodal approximations of 39 measured soil water retention
and hydraulic conductivity characteristics collected in volcanic Vesuvian soil located in the Campania Region
Plain (Naples, Southern Italy)
Plot-scale modeling of soil water dynamics and impacts of drought conditions beneath rainfed maize in Eastern Nebraska
No full textPrediction of the saturated hydraulic conductivity from Brooks and Corey's water retention parameters.
No full textPrediction of flow through variably saturated porous media requires accurate knowledge of the soil hydraulic properties, namely the water retention function (WRF) and the hydraulic conductivity function (HCF). Unfortunately, direct measurement of the HCF is time-consuming and expensive. In this study we derive a simple closed-form equation that predicts the saturated hydraulic conductivity, Ks from the WRF parameters of Brooks and Corey (1964). This physically based analytical expression uses an empirical tortuosity parameter () and exploits the information embedded in the measured pore-size distribution. Our proposed model is compared against the current state of the art using more than 250 soil samples from the GRIZZLY and HYPRES databases. Results demonstrate that the proposed model provides better predictions of the saturated hydraulic conductivity values with reduced size of the 90 % confidence intervals of about three orders of magnitude
Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe.
No full textStudy Region: Europe. A total of 660, 522, and 4940 soil samples belonging to GRIZZLY, HYPRES, and EU-HYDI databases, respectively, were used for parametric evaluation. The functional evaluation was performed in two study sites located in southern Italy.
Study Focus: The soil water retention and hydraulic conductivity functions are crucial input information to land surface models. Determining these functions by using direct methods is hampered by excessive time and unaffordable costs required for field activities and laboratory analyses. Pedotransfer functions (PTFs) are widely-used indirect techniques enabling the soil hydraulic properties to be predicted by using easily-retrievable soil information. In a parametric evaluation, the predictive capability of PTFs is examined by comparing measured and estimated soil water retention parameters and saturated hydraulic conductivity. Yet, information about the performance of PTFs for specific modeling applications is mandatory to evaluate the PTF effectiveness more in-depth. This approach is commonly defined as functional evaluation.
New Hydrological Insights for the Region: The best performing four PTFs selected in the parametric evaluations are tested under two functional evaluations. The first encompasses a spatial interpolation with a geostatistical technique, whereas the second employs Hydrus-1D to simulate the water balance components along an experimental transect. Our results reinforce and integrate the insights of previous studies about the use of a PTF and stress on the ability, or inability, of this technique to adequately reproduce the observed spatial variability of the soil hydraulic properties and simulated water fluxes
Root-zone water-storage capacity and uncertainty: An intrinsic factor affecting agroecosystem resilience to drought.
No full textMapping ecosystem function indicators helps identify areas susceptible to drought, heat stress, and reduced agricultural production. This information can be used to prioritize areas for targeted interventions to tackle adverse climatic conditions and changes in land use. Root-zone water-storage capacity (SR) is a commonly used variable of agroecosystem functioning, representing the maximum value of water stored within the root zone and accessible to vegetation for its productive growth. Mapping SR over large spatial scales is only feasible through an oversimplification of real-world conditions. Under such circumstances, we propose to resort to soil-hydraulic-energy indices, namely the integral mean water capacity (IMWC) and the integral energy (IE) and an effective root-zone depth (zR). Accordingly, more efficient and environmentally sensitive, albeit still simplistic, determination of the root-zone water-storage capacity is computed as SR,IMWC = zRxIMWC, and validated against soil moisture measurements carried out along a transect. Subsequently, the SR,IMWC indicator was mapped in Campania, a 13,700 km2 region in southern Italy. This study also addressed the issue of the propagation of epistemic uncertainty in input soil hydraulic parameters to the output response variable IMWC. This was accomplished using a Monte Carlo simulation technique that generated several equiprobable stochastic realizations from the multivariate set of data inputs. Finally, we assessed the potential utility of the integral capacity energy (ICE) composite indicator, computed as the ratio IMWC/IE in %, as a scoring parameter to identify Priority Intervention Areas (PIAs) where resilience to environmental challenges, including water scarcity, drought events, and post-fire conditions, could be enhanced
Use of a flux-based field capacity criterion to identify effective hydraulic parameters of layered soil profiles subjected to synthetic drainage experiments.
No full textThis study explores the feasibility of identifying the effective soil hydraulic parameterization of a layered soil profile by using a conventional unsteady drainage experiment leading to field capacity. The flux-based field capacity criterion is attained by subjecting the soil profile to a synthetic drainage process implemented numerically in the Soil-Water-Atmosphere-Plant (SWAP) model. The effective hydraulic parameterization is associated to either aggregated or equivalent parameters, the former being determined by the geometrical scaling theory while the latter is obtained through the inverse modeling approach. Outcomes from both these methods depend on information that is sometimes difficult to retrieve at local scale and rather challenging or virtually impossible at larger scales. The only knowledge of topsoil hydraulic properties, for example as retrieved by a near-surface field campaign or a data assimilation technique, is often exploited as a proxy to determine effective soil hydraulic parameterization at the largest spatial scales. Comparisons of the effective soil hydraulic characterization provided by these three methods are conducted by discussing the implications for their use and accounting for the trade-offs between required input information and model output reliability. To better highlight the epistemic errors associated to the different effective soil hydraulic properties and to provide some more practical guidance, the layered soil profiles are then grouped by using the FAO textural classes. For the moderately heterogeneous soil profiles available, all three approaches guarantee a general good predictability of the actual field capacity values and provide adequate identification of the effective hydraulic parameters. Conversely, worse performances are encountered for the highly variable vertical heterogeneity, especially when resorting to the “topsoil only” information. In general, the best performances are guaranteed by the equivalent parameters, which might be considered a reference for comparisons with other techniques. As might be expected, the information content of the soil hydraulic properties pertaining only to the uppermost soil horizon is rather inefficient and also unable to map out the hydrologic behavior of the real vertical soil heterogeneity since the drainage process is significantly affected by profile layering in almost all cases
Functional evaluation of a simplified scaling method for assessing the spatial variability of the soil hydraulic properties at hillslope scale.
No full textMapping soil hydraulic parameters with traditional scaling methods that use laboratory-determined hydraulic characteristics (LAB-method) is not always feasible as it involves expensive, time-consuming and sophisticated measurements on soil samples collected in several locations of the study area. An alternative scaling method (AP-method) has been recently proposed to indirectly retrieve the soil hydraulic properties following the Arya-Paris physico-empirical pedotransfer function, which makes use of particle-size distributions and bulk density values. In this synthetic study we verify the performance of the AP-method from a functional perspective, by evaluating the differences in the simulated soil water budget through a Monte Carlo approach. Notwithstanding the AP-method can provide soil hydraulic property patterns with faster experimental procedures and minor costs, we observe significant bias in the predicted spatially-averaged soil water budget due to a poor parametric calibration of the AP-method and an imprecise identification of the spatial correlation structure of the AP-estimated scaling factors
How effective is bimodal soil hydraulic characterization? Functional evaluations for predictions of soil water balance.
No full textTo overcome some drawbacks of the unimodal relations commonly used to describe soil hydraulic properties (SHPs), previously we developed bimodal lognormal relations that have the following main features: (i) they are closed-form expressions, (ii) they have a sound theoretical basis and provide a more general conceptualization of soil and (iii) they improve the description of both the water retention (WRF) and hydraulic conductivity (HCF) functions. Nevertheless, the reliability of soil hydraulic analytical relations is often tested only at the curve fitting level. Comparisons between unimodal and bimodal soil hydraulic relations are more effective and informative when performed within a functional evaluation approach. We use the HYDRUS-1D package to quantify and compare soil moisture dynamics and storage regimes for hydrological processes at both the event and annual time-scales when the soil domain is characterized by either unimodal or bimodal hydraulic properties. Seven soil samples taken from a previous study were used in numerical simulations of drainage or infiltration processes; there were large relative discrepancies in terms of simulated soil water storage. A subsequent test that involved simulations of soil water budget for the period 2000–2012 was implemented for a peach-orchard field by a conventional scaling method. This test also enables soil spatial variation to be taken into consideration. Two different scenarios enable the epistemic uncertainty to be evaluated when different hydraulic models are considered for soil with weak or strong bimodality. With Willmott's refined index of agreement, discrepancies in soil water storage were about 15% (weak bimodality) or more than 30% (strong bimodality).
Highlights:
Main aim of this study is the assessment of epistemic uncertainty in modelling soil water dynamics.
We make functional evaluations for both event-based and long-term hydrological processes.
Disregard of bimodal soil hydraulic behaviour can lead to large epistemic errors.
Better predictions of soil hydraulic properties should be sought in future research
Developing pedotransfer functions for predicting soil bulk density in Campania.
No full textOven-dry soil bulk density (BD) is a key soil parameter in biophysical models. Yet, its direct determination for large-scale modeling applications is limited by excessive efforts required for labor-demanding, time-consuming, expensive field campaigns and laboratory-based measurements. To circumvent these shortcomings, BD can be estimated using pedotransfer functions (PTFs) that, however, offer their optimal prediction capability if calibrated and validated within the area of interest. In this study, we exploited the availability of a dataset comprising 3,316 soil samples collected in the farmlands of Campania (a region of southern Italy) to develop regional PTFs for predicting BD using the Random Forest (RF) algorithm. RF was executed considering different combinations of seven soil and three terrain attributes with a 10-fold cross-validation approach to avoid performance overestimation. In light of the RF-based results, we further developed two new PTFs based on multiple linear regression equations. The first regression-based PTF was multiparametric and employed eight features (i.e., six soil properties and two terrain features as environmental covariates), whereas the second PTF was parsimonious and based on three easily available soil predictors. Both regression-based PTFs consistently outperformed 62 existing published PTFs. We also enhanced PTF prediction capabilities by employing regionalization through a clustering approach by grouping soil samples in ten land system classes. Finally, transferability of our models was tested using an external large independent dataset of 12,019 soil samples extracted from the European EU-HYDI database. The parsimonious PTF proved satisfactory prediction performance by corroborating results found in the Campania dataset
La capacità di immagazzinamento idrico del suolo come indicatore di resilienza degli agro-ecosistemi.
No full textSi propone l’impiego della capacità di immagazzinamento idrico del suolo come indicatore di resilienza per ecosistemi agro-forestali.
Aspetto innovativo dell’indicatore è considerare l’intera funzione di ritenzione idrica del suolo e calcolare la profondità efficace del suolo in base ai fattori bioclimatici locali.
Il metodo è stato applicato per mappare i caratteri di resilienza del territorio agro-forestale della Regione Campania
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