Actual Spatial Variability Research Articles

Analyzing the role of soil and vegetation spatial variability in modelling hydrological processes for irrigation optimization at large scale

The main purpose of this paper was to study the effect of spatial variability of soil hydraulic properties (HP) and vegetation parameters (VP) (e.g., leaf-area index, LAI, and crop coefficient, Kc) on modelling agro-hydrological processes and optimising irrigation volumes at large scale. Based on this analysis, the effect of partly overlooking the spatial variability of soil HP and/or VP inputs was verified on a 140 ha irrigation sector in “Sinistra Ofanto” irrigation system in Apulia Region, Southern Italy. Five soil profiles were excavated and the HP were measured in all the soil horizons. Additionally, measurements of soil HP were taken in the surface soil layer in ninety sites distributed over the whole irrigation sector. All the HP measurements were carried out using tension infiltrometer. Remote sensing applications were used to obtain LAI and Kc using European Space Agency’s (ESA) Sentinel-2 images with 10 m resolutions. First, distributed (on ninety polygons with an average area of about 1.5 ha) optimal irrigation volumes and related deep percolation volumes at a depth of 80 cm, were computed using an agro-hydrological model and accounting for the actual observed variability of soil HP and VP inputs. The sector scale irrigation and deep percolation volumes were obtained by aggregating the distributed irrigation volumes. This was considered as the reference scenario (hereafter DVS—Detailed Variability Scenario). Then, reduced variability scenarios (hereafter RVS—Reduced Variability Scenario) were considered, where the information on the actual spatial variability of the soil HP and VP was gradually overlooked to find the minimum data set needed to still have sector scale irrigation volumes and related deep percolation volumes comparable to those obtained under the DVS. Results showed that overlooking VP (RVS-VP) variability did not significantly change the optimal irrigation volumes and the deep percolation fluxes. By contrast, neglecting the HP variability (RVS-HP) showed significant effects on both the irrigation and percolation volumes compared to the DVS. The main practical finding was that, at least for the area investigated in this study, hydraulic characterization of one soil profile in an area of approximately 30 ha provides sector scale irrigation volumes and percolation fluxes comparable to those obtained under the DVS, thus by accounting for all the observed local variability.

A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index

Land Surface Models which determine evapotranspiration (ET) by neglecting the sub-grid heterogeneity of land-atmosphere parameters will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. This study evaluated in detail the aggregation biases in the tile mean ET estimates due to applying bulk-averages of the Saturation degree of surface Soil Moisture (SSM) and Leaf Area Index (LAI) for each tile through numerical experiments. Four types of Probability Distribution Function (PDF) were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces. Aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the PDFs, which complies with energy conservation. In addition, a wide range of meteorological conditions was applied in our numerical experiments and the impacts were evaluated by binning results according to the reference ET (ET0). We found that potentially significant bias can be found in semi-arid areas. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0).

Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy

For decades, the importance of evapotranspiration processes has been recognized in many disciplines, including hydrologic and drainage studies, irrigation systems design and management.In this research, the suitability of the Prediction Of Worldwide Energy Resource database published by the American National Aeronautics and Space Administration (POWER-NASA), to estimate daily meteorological variables and ET0 was assessed in Sicily, Italy, for the period 2006–2014, based on ground data measured by a network of climate stations belonging to the regional Agro-meteorological Information Service (SIAS). After comparing the climate data (minimum, Tmin, maximum, Tmax, and average, Tavg, air temperature, relative air humidity, RH, global solar radiation, Rs and wind speed, u) available in both databases, a statistical comparison was carried out on ET0 values estimated according to the Penman Monteith equation in the version proposed by the Food and Agriculture Organization (FAO-56 PM).The analysis showed that correlations between air temperature and relative air humidity from both databases are affected by elevation of the weather stations and the distance from the sea. In addition, ET0 values estimated with POWER-NASA database were generally comparable to those obtained by using the SIAS records, with RMSE values ranging between 0.68 and 1.27mmd−1 and MBE varying between −0.39 and 0.73mmd−1. The greatest differences in ET0 values are due to the resolution of POWER-NASA archive (1° latitude by 1° longitude), that cannot detect the actual spatial variability observed on ground, as well as to inaccurate estimations of relative air humidity occurring for the coastal weather stations as well as of the air temperature for those inland stations characterized by high elevations. However, the achieved results support the possibility of obtaining suitable estimates of daily ET0 based on the POWER-NASA agro-climatology archive, even to other Mediterranean countries where most of the climate variables are not measured.

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME (0.1513) , RMSE (95.91) , ASE (101.84) , MSE (-0.0036) , and RMSSE (1.0397) , were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.

On the uniformity of anthropogenic Venice uplift

AbstractA programme of Venice uplift because of seawater injection into a 600–800 m deep brackish aquifer underlying the lagoon has recently been advanced. As the actual spatial variability of the hydro‐geological parameters is to a large extent unknown, a controversial issue concerns the 25–30 cm heave prediction with the possible generation of differential displacements that might jeopardize the integrity of the architectural patrimony. It is shown that significant differential displacements have occurred in Venice in the past and are still presently occurring as evidenced by SAR interferometry. The results of a stochastic analysis addressing the variability of the hydraulic conductivity of the injected formation indicate that even a highly uneven aquifer expansion does not migrate to ground surface because of the smoothing effect exerted by the overburden. The predicted differential displacements are well below the values Venice is experiencing. Any a priori alarmism appears to be unfounded and unjustified.

Discussion of "Bearing capacity prediction of spatially random c – ϕ soils"

Popescu The writer would like to commend the authors for a particularly interesting analytical and numerical study of the effect of spatial variability of soil properties on the bearing capacity. It is generally acknowledged that the problem of accounting for the stochastic spatial variation of soil properties is a particularly complex one, both from the datacollection point of view and from the methodological point of view. Generally accepted techniques have not yet been established to accomplish these tasks. To initiate a constructive discussion on this subject, some comments are provided here on the selected range of coefficients of variation (COVs) of soil properties and on the methodology suggested by the authors to simulate the stochastic soil properties. Range of coefficients of variation Confidence in design soil properties, and therefore in predicted structural response, is affected by a series of uncertainties arising from several sources, such as intrinsic spatial variability (i.e., actual variation of soil properties from one location to another), measurement errors, and insufficient field information. Those sources of uncertainty are often considered together as one source of variability in the response. It was observed by the authors, and by other researchers, that the intrinsic spatial variability of soil properties affects not only the variability of the predicted structural response, but also the mechanical behaviour itself. As explained, for example, by Focht and Focht (2001), because of the presence of weaker zones in a soil deposit, “the actual failure surface can deviate from its theoretical position to pass through weaker material so that the average mobilized strength is less than the apparent average strength.” It appears that different types of uncertainties (insufficient information, measurement errors, etc., on the one hand, and actual spatial variability, on the other hand) affect the structural response in different manners, and they may have to be analyzed separately. It is the understanding of the writer that this was in fact the intention of the authors, namely to analyze the effects of intrinsic spatial variability alone. A number of researchers have provided information on ranges of COVs for spatial variability of different soil properties, as obtained from in situ soil investigations. In most cases, those ranges also include variability due to measurement errors. There is, however, a very comprehensive study in this respect (Phoon and Kulhawy 1999), mentioned also by the authors, in which approximate guidelines are provided for the COV of intrinsic spatial variability, separated from other sources of uncertainty. It is the writer’s opinion that, until more results become available, the guidelines provided by Phoon and Kulhawy (1999) are a good indication of the order of magnitude for the COV of soil variability. COVs as high as 5 (i.e., one order of magnitude larger than those usually reported in the literature) are assumed in the paper for the soil parameters. In justifying this range, the authors present a very interesting set of considerations related to soil variability at various scales, starting from the soil particle scale (of the order of micrometres) and going to the regional scale (of the order of kilometres). It is demonstrated that, within such limits, the range of COVs assumed in the paper is reasonable. The writer believes, however, that with respect to intrinsic soil spatial variability, the scales applicable here are bounded by the volume of soil involved in the bearing capacity failure and by the finite element mesh size. 367

Sensitivity of Average Annual Runoff to Spatial Variability and Temporal Correlation of Rainfall

This paper examines the sensitivity of annual area mean runoff calculations to the effects of spatial variability and temporal correlation of rainfall. The model used is based upon the hypothesis that the annual water balance is determined only by rainfall, potential evapotranspiration, and soil water storage. A simple bucket hydrology model with a seasonally varying potential evapotranspiration is used with rainfall data measured at several sites on the Delmarva Peninsula. Annual area mean runoffs are calculated for three cases: 1) actual spatial variability among the rain gauge sites and temporal correlation between consecutive 1-min rainfall amounts are maintained (the actual case); 2) actual spatial variability among the sites is maintained but temporal correlation between the consecutive 1-min rainfall amounts is minimized (the site-shuffled case); and 3) both spatial variability and temporal correlation are ignored (the area-averaged case). The actual case represents the baseline for comparison with the other two cases. The annual a” mean runoffs show little sensitivity to spatial variability and temporal correlation for this model. Therefore, if finite soil permeability effects are ignored in favor of simple water storage capacity, then spatial variability and temporal correlation of rainfall appear to have little impact on the annual area mean runoff for the data considered in this study.