Spatial Variability Of Parameters Research Articles

Applying Spatial Geostatistical Analysis Models for Evaluating Variability of Soil Properties in Eastern Shiraz, Iran

ABSTRACT- The information on the spatial properties of soil is vital to improve soil management and to increase the crop productivity. Geostatistical analysis technique is one of the most important methods for determining the spatial properties of soil. The aim of this study was to investigate spatial variability of soil chemical and physical attributes for field management in eastern Shiraz, Iran, in 2010. In the study area, for applying geostatistical analysis, eighty soil samples were taken randomly. The variability of saturation percentage (SP), electrical conductivity (EC), soil pH, sand%, silt%, clay%, nitrogen (N), phosphorus (P) and potassium content (K) of the soil used to determine the spatial properties of soil by geostatistical analysis techniques. Soil properties were analyzed both geostatistically and statistically on the basis of the Semivariogram models. Thus, each soil parameter was used for different Semivariogram models such as spherical, circular and exponential because of their different spatial structures. The results showed that the best model to generate soil properties map was ordinary kriging with spherical and exponential Semivariogram models. The best model for soil pH, SP, K and N was the spherical model whereas for sand%, silt%, clay%, EC and P, the best model was the exponential model. Based on the models, the range of spatial dependency was found to vary within soil parameters. EC had the longest (134 meter) and pH had the shortest (19.1 meter) range of spatial dependency. Additionally, spatial patterns may vary among soil parameters in the study area. Therefore, Semivariogram models can be useful tools to determine spatial variability of parameters, preparing soil map and field management strategy.

Variabilidade espacial de propriedades hidrodinâmicas de um Neossolo Regolítico sob pastagem e caatinga

As propriedades hidrodinâmicas dos solos de pastagem e de caatinga, são afetadas por diversos fatores, sobretudo associados a aspectos espaciais e temporais relacionados às diferentes características dos sistemas radiculares e das taxas de pisoteio do gado, mas poucos resultados que avaliem o efeito da magnitude dessas fontes de variabilidade sobre as propriedades hidrodinâmicas dos solos têm sido relatados na literatura. Objetivou-se analisar a variabilidade espacial dos parâmetros que descrevem as curvas de retenção (van Genuchten) e da condutividade hidráulica (Brooks & Corey) da água do solo, obtidos com a metodologia denominada "Beerkan", que se baseia em ensaios de infiltração simplificados e nas informações texturais do solo. Para isto foram realizadas medidas de infiltração e coletadas amostras de solo em duas áreas: uma área de pastagem (Brachiaria decumbens) e outra de caatinga. Os resultados mostraram que o uso do solo com pastagem alterou significativamente as propriedades analisadas quando comparadas com as do solo com caatinga, com alterações mais expressivas na densidade do solo e na condutividade hidráulica. A análise geoestatística identificou a existência de dependência espacial de alguns atributos estudados, permitindo o mapeamento das áreas experimentais.

Random field-based reliability analysis of prestressed concrete bridges

A method for reliability assessment of prestressed concrete bridges accounting for the spatial variability of structural parameters is presented in this paper. The spatial variability of these parameters is modeled by means of random fields and the reliability estimates were determined by a generalized first-order reliability algorithm. The main feature of the random field-based reliability method is to consider the spatial variability of structural parameters that is disregarded in the traditional reliability method for prestressed concrete bridges. The accuracy and efficiency of the random field-based reliability method is demonstrated through two numerical examples. A software strategy for interfacing the random field-based reliability method with ANSYS is developed via a freely available MATLAB software tool (FERUM). As a practical engineering example, the reliability of a prestressed concrete bridge is illustrated. The obtained results clearly show the applicability and merits of the random field-based reliability method and the associated software strategy.

Variability and Trends in the Duration and Depth of Snow Cover in Iran in Thirty Years

This study investigates the changes and variability of the number of days with snow cover of ≥ 1 cm depth and the seasonal maximum snow cover depth at 28 meteorological stations throughout Iran, spanning a period of 30 (1981-82 to 2010-2011) to 15 (1996-97 to 2010-2011) Winter seasons. Investigation of spatial variability of parameters using GIS map shows that maximum of the snow depth and the number of days with snow cover belongs to Koohrang station in Zagros mountains about 69.4 cm and 2457 days in 15 winter season, respectively. A decreasing trend of snow cover in the 30 winter seasons is observed in areas with abundant snow cover in highland stations. However, the scale of the snow cover change is not proportional to the air temperature changes in the most stations.

Quantifying the spatial variability of soil physical and chemical properties in relation to mitigation of diffuse water pollution

Understanding spatial variability of soil properties in response to land-use impacts is essential for evaluating the effectiveness of measures taken to address diffuse water pollution from agriculture. However, despite the growing emphasis on integrated catchment-scale implementation of land-use mitigation measures, the baseline landscape-scale evaluation of the spatial variability of key soil nutrients remains scarce. This study employs a high resolution geostatistical approach to characterise the spatial variability of parameters, including soil bulk density (BD), total soil carbon (TC), nitrogen (TN), phosphorus (TP), inorganic phosphorus (IP), organic phosphorus (OP), stable nitrogen isotope ratio (δ15N), C:N ratio, carbon storage and nitrogen storage in two study catchments with contrasting land uses (agricultural and semi-natural) that are subject to targeted management interventions to reduce flood risk and improve water quality. We found a stronger degree of spatial dependence of all soil properties in the agricultural than the semi-natural catchment, except for bulk density and δ15N. Furthermore, bulk density, TP, IP, OP, C:N ratio, δ15N and carbon storage showed a longer range or spatial auto-correlation in the agricultural catchment. The central tendency (median and mean) of all soil properties was also significantly different between the two catchments, with the exception of IP and δ15N. The spatial correlations between the soil properties pointed to the mechanisms that were responsible for the observed differences, whilst the krigged surfaces of soil variables identified most likely critical source areas for targeted land management interventions to improve water quality. Arable and intensive grasslands were identified as ‘high-impact’ land uses, associated with negative alteration of soil properties and increased diffuse water pollution, whilst moorland was a ‘low impact’ land use associated with improved water quality. A comparison with the national soil survey dataset shows that whilst it can be relied upon for the broad characterisation of carbon and TP stocks in the two study catchments, it underestimates the spatial variability of key soil properties in certain soil types and land uses. As the restoration of soil spatial heterogeneity may take several decades, a high resolution geostatistical approach should be included in the future design of catchment-scale monitoring schemes to inform catchment management strategies and elucidate the time frame over which landscape scale improvements in soil properties and corresponding ecosystem services can be achieved.

A Bayesian hierarchical approach to regional frequency analysis

Regional frequency analysis (RFA) has a long history in hydrology, and numerous distinct approaches have been proposed over the years to perform the estimation of some hydrologic quantity at a regional level. However, most of these approaches still rely on strong hypotheses that may limit their application and complicate the quantification of predictive uncertainty. The objective of this paper is to propose a general Bayesian hierarchical framework to implement RFA schemes that avoid these difficulties. The proposed framework is based on a two‐level hierarchical model. The first level of the hierarchy describes the joint distribution of observations. An arbitrary marginal distribution, whose parameters may vary in space, is assumed for at‐site series. The joint distribution is then derived by means of an elliptical copula, therefore providing an explicit description of the spatial dependence between data. The second level of the hierarchy describes the spatial variability of parameters using a regression model that links the parameter values with covariates describing site characteristics. Regression errors are modeled with a Gaussian spatial field, which may exhibit spatial dependence. This framework enables performing prediction at both gaged and ungaged sites and, importantly, rigorously quantifying the associated predictive uncertainty. A case study based on the annual maxima of daily rainfall demonstrates the applicability of this hierarchical approach. Although numerous avenues for improvement can already be identified (among which is the inclusion of temporal covariates to model time variability), the proposed model constitutes a general framework for implementing flexible RFA schemes and quantifying the associated predictive uncertainty.

Foot placement variability as a walking balance mechanism post-spinal cord injury

BackgroundSpinal cord injury affects walking balance control, which necessitates methods to quantify balance ability. The purposes of this study were to 1) examine walking balance through foot placement variability post-injury; 2) assess the relationship between measures of variability and clinical balance assessments; and 3) determine if spatial parameter variability might be used as a clinical correlate for more complex balance measurements. MethodsTen persons with spinal cord injury walked without devices on a split-belt treadmill at self-selected speeds. Ten healthy controls walked at 0.3 and 0.6m/s for comparison. Variability of step width and length, anteroposterior and mediolateral foot placements relative to center-of-mass, and margin-of-stability were calculated. Clinical assessments included Berg Balance Scale and Dynamic Gait Index. FindingsParticipants with spinal cord injury demonstrated significantly different variability in all biomechanical measures compared to controls (P≤0.007). Berg Balance Scale scores were significantly inversely associated with step length as well as anteroposterior and mediolateral foot placement variability (P≤0.05). Dynamic Gait Index scores were significantly inversely associated with mediolateral foot placement variability (P≤0.05). Participants with spinal cord injury showed significant correlations between spatial parameter variability and all other measures (P≤0.005), except between step length and margin-of-stability (P=0.068); controls revealed fewer correlations. InterpretationPersons post-spinal cord injury exhibit an abnormal amount of stepping variability when challenged to walk without devices, yet preserve the ability to avoid falling. When complex laboratory measures of variability are unavailable clinically, spatial parameter variability or standardized balance assessments may be plausible indicators of walking balance control.

Model parameter analysis using remotely sensed pattern information in a multi-constraint framework

► Describes a catchment scale coupled groundwater/surface-water/atmosphere model. ► Examines the application of satellite data to improve process characterization. ► Sensitivity analysis performed using distributed versus in situ observations. ► Only distributed evaluation shows sensitivity to spatial variability of parameters. ► Identifies the benefit of spatial patterns in constraining distributed models. The development of sophisticated distributed hydrological models that characterize groundwater, surface water and atmospheric interactions, inevitably increases the amount of observational data required to force the model and evaluate simulations. The limitations of using traditional evaluation data and the benefits of including independent and spatially distributed observational constraints are illustrated here through a sensitivity analysis of a coupled surface-water/groundwater/atmosphere model simulated at the catchment scale. In the analyses, model performance based on objective functions using conventional stream flow and groundwater head observations are compared against objective functions that utilize spatially distributed satellite based surface temperature retrievals as the calibration variable. The advantage of incorporating remote sensing based observations into the model evaluation process is their spatially distributed information content, enabling an assessment of the capacity of the model to reproduce observed spatial patterns. Results indicate that employing spatially distributed model parameterizations has limited impact on improving model performance when evaluated against traditional model objectives of stream flow and groundwater head. Indeed, a spatially uniform parameterization produced almost identical model performance. In contrast, objective functions that incorporate remote sensing based surface temperatures, highlighted the comparatively poor reproduction of spatial patterns when using a spatially uniform parameterization. Although lumped observations such as stream discharge contain valuable information regarding the total catchment water budget, such traditional observations should be merged with independent data sets that incorporate spatial pattern information to strengthen the robustness of model performance and model parameter constraint.

On the ability to infer spatial catchment variability using streamflow hydrographs

Spatially distributed models can potentially provide improved hydrologic predictions because of their ability to exploit spatially distributed data while providing estimates of hydrologic variables at interior catchment locations. However, attempts to estimate spatially distributed parameter fields via model calibration have been fraught with difficulty. This paper examines the factors that can influence the ability to infer spatial properties of a distributed model when the only information available for model evaluation is catchment streamflow response. In particular, we investigate the conditions under which spatial variability in parameters and rainfall cause sufficiently strong variations in the streamflow hydrographs to justify their representation in catchment models and whether such information can be detected via commonly used model performance measures. Our results show that spatial variability in parameter and precipitation fields can, indeed, have a detectable impact on the properties of the streamflow hydrograph but that this impact can be so greatly diminished by the damping and dispersive effects of routing that it is virtually nondetectable by conventional performance measures by the time the water reaches the catchment outlet. And although measures based on information theory may be able to detect subtle variations of this kind, the information may not ultimately be useful in the face of model structure and data errors. The only reasonable way forward therefore is to explore other kinds of catchment information (including multiple interior flow gauging locations) for use in estimation of spatially distributed parameter fields.

Soil Spatial Variability of Parameters in Soil Layer

Soil Spatial Variability of Parameters in Soil Layer

On the use of spatial regularization strategies to improve calibration of distributed watershed models

Hydrologic models require the specification of unknown model parameters via calibration to historical input‐output data. For spatially distributed models, the large number of unknowns makes the calibration problem poorly conditioned. Spatial regularization can help to stabilize the problem by facilitating inclusion of additional information. While a common regularization approach is to apply a scalar multiplier to the prior estimate of each parameter field, this can cause problems by simultaneously changing both the mean and the variance of the distribution. This paper explores a multiple‐criteria regularization approach that facilitates adjustment of the mean, variance, and shape of the parameter distribution, using prior information to constrain the problem while providing sufficient degrees of freedom to enable model performance improvements. We also test simple squashing functions to help in maintaining conceptually reasonable parameter values throughout the spatial domain. We apply the method to three basins in the context of the Distributed Model Intercomparison Project (DMIP2), obtaining considerable performance improvements at the basin outlet. However, the prior parameter estimates are found to give much better performance at the interior points (treated as ungauged), suggesting that the spatial information has not been properly exploited. The results also suggest that basin outlet hydrographs may not be particularly sensitive to spatial parameter variability and that an overall basin mean value may be sufficient for flow forecasting at the outlet, although not at the interior points. We discuss weaknesses in our study approach and suggest diagnostically more powerful strategies to be pursued.

Spatial variability-based corrosion risk assessment and strategy of repair and maintenance for RC structures

A corrosion risk assessment model (CRAM) of reinforced concrete structures subjected to marine environment is presented. The spatial variability of parameters, which are decisive for the durability performance, is modelled probabilistically in terms of sample statistics, which may be assessed by testing of existing structures. The result of the chloride profile test is suggested to update the input model parameters for future structural condition prediction. The requirements to the service life performance are formulated in terms of the probability that the structure will be in a certain condition class after a given service time. Further, it is demonstrated how optimal strategies for repair and maintenance of concrete structure subject to chloride ingress can be determined on the basis of corrosion risk classes. The present method to plan optimal repair strategy is illustrated by an assessment case. For structures with large concrete surface exposed to chloride-laden environment, both spatial and temporal characteristics of deterioration are suggested to consider in condition assessment based on field inspection.

Variabilidade espacial dos parâmetros hidrodinâmicos de duas parcelas agrícolas no estado da Paraíba

O conhecimento das curvas de retenção e da condutividade hidráulica da água do solo é indispensável para a modelagem do transporte de água e de poluentes em sistemas agrícolas. Essas propriedades são afetadas por numerosas fontes de variabilidade, geralmente associadas a fatores espacial e temporal, e relacionadas com o manejo agrícola. O objetivo desse trabalho foi analisar a variabilidade espacial dos parâmetros das curvas de retenção da água (van Genuchten) e da condutividade hidráulica (Brooks & Corey) da água do solo, obtidos com o método "Beerkan", que se baseia em ensaios de infiltração simplificados e nas informações texturais do solo. Esses parâmetros são de forma ou texturais (parâmetros de ajuste que dão forma às curvas) e de normalização ou estruturais (umidade volumétrica saturada, condutividade hidráulica saturada do solo e potencial de entrada de ar). Os ensaios de campo foram conduzidos em uma malha regular de 25 x 25 m num Neossolo Flúvico e num Latossolo Amarelo cultivados com feijão (Vigna unguiculata (L.) Walp.). Para ambos os solos, os valores dos parâmetros de forma, da umidade volumétrica saturada, dos logaritmos da condutividade hidráulica saturada e do potencial de entrada de ar apresentaram dependência espacial. Os valores da umidade volumétrica saturada e dos logaritmos do potencial de entrada de ar foram mais sensíveis à variação a pouca distância no Neossolo Flúvico do que do Latossolo Amarelo, com semivariogramas apresentando estruturas de variação com fraca dependência espacial.

Stochastic evaluation of the damage length in RC beams submitted to corrosion of reinforcing steel

The influence of spatial variability of parameters appearing in a model of concrete carbonation (and resulting rebars corrosion) is studied. For this purpose, random fields are introduced in order to represent the input parameters of the model. The concept of damage length is defined as the portion of a beam structure for which a durability failure criterion is attained. Analytical derivations inspired from time-variant reliability methods lead to tractable formulæ for the mean and standard deviation of the damage length. Results are validated using Monte Carlo simulation (MLS) of both the input random fields (after proper discretization) and the resulting damage length. The histogram of the damage length, which is obtained as a byproduct of the MCS, is finally commented.

Quantification of infiltration processes in urban areas by accounting for spatial parameter variability

As a consequence of human living and activity, water infiltration to the urban subsurface occurs from a variety of different sources, like precipitation, irrigation, leaking pipes and sewers, septic tanks and rainwater infiltration ponds. This infiltration is strongly related with quality issues of the infiltrated water and further impact on groundwater quality. In order to set up an integrated urban water balance it becomes essential to estimate the infiltration processes, i.e. water flow and solute transport, from these different infiltration sources and to take into account the large spatial variability of sediment properties, the geometric settings of these sources and the groundwater table. For that purpose, the development of simple, physically-based quantification approaches is required in order to establish an efficiently working prediction and risk analysis tool within the framework of an integrated urban water management system. The scope of the presented work was to demonstrate the applicability of the developed approaches at urban scale. Since a detailed, three-dimensional, numerical quantification of the infiltration processes within the entire urban area is not possible, the individual sources were considered as independent within the EU AISUWRS project. Different models were developed for balancing infiltration from areal and point sources with respect to the related flow pattern. The analytical model UL_FLOW, based on one-dimensional, steady state analytical solutions, allows the estimation of conservative tracer residence times in layered sediments under varying infiltration rates. The numerical model WSTM, based on a three-dimensional random walk approach, calculates water and solute transport from pipe leaks. Additionally, the sources were classified in accordance to the spatial distribution of the parameters determining the infiltration processes. UL_FLOW was applied to data sets from the city of Rastatt within a case study of the AISUWRS project. Each neighbourhood of water balance computation by the Urban Volume and Quality Model (UVQ) was defined as an areal infiltration source with unique parameter values for sediment depth, profile and properties, as well as infiltration rate time series. Groundwater recharge and residence time series were computed for each neighbourhood. Relevant statistical parameters obtained by time series analyses from those time series could be mapped by GIS. Point infiltration, particularly from sewers, was classified due to the sediment parameters and the distance to the groundwater table at each source location in order to reduce computational efforts. WSTM computations provided time series of groundwater recharge and tracer breakthrough for some specific cases. The analytical model UL_FLOW provides fast and efficient computation of groundwater recharge and residence times accounting for storage effects within the unsaturated zone of urban areas. The reliability of this model has been shown by cross validation with HYDRUS1D. Because of the high computational effort, WSTM could provide only short-term simulations for some specific parameter sets for which residence time estimates could be derived. UL_FLOW provides an analytical modelling tool for balancing one-dimensional areal infiltration and estimating residence times under varying conditions including spatial parameter variability. These balances could be used for assessing the impact of those infiltration sources on groundwater quality. The tracer breakthrough from point infiltration sources computed by WSTM could also be used for such kinds of assessment. The larger spatial parameter variability associated with these sources could be handled by classification in GIS environments. Similar to the areal sources, a simple balance approach for point sources based on analytical solutions needs to be developed for estimating residence times in order to avoid large computational efforts. Such a model would complete the balancing of all kinds of infiltration sources in urban areas efficiently. Since the approaches are based on the balance of the physical processes, they have a large predictive capability and could be included into an integrated urban water balance and management system. The mapping of the statistical values of the residence times provides a tool to compare parts of the urban areas and to visualize differences between urban water management scenarios.

Reliability analysis of retaining walls designed to British and European standards

The uncertainties involved in geotechnical design are considered. Using a formulated approach that accounts for model uncertainty, variable correlation and spatial variability, failure probabilities associated with alternative designs of retaining walls are calculated. Application of this approach is also demonstrated through a case study that is based on a real construction project. The study provides evidence that failure probabilities are significantly influenced by considerations regarding the spatial variability of parameters and the model uncertainty, as well as the adopted statistical modelling of the variables. Furthermore, the study facilitates observations regarding the calibration of codes of practice for the design of retaining walls.

Type‐curve estimation of statistical heterogeneity

The analysis of pumping tests has traditionally relied on analytical solutions of groundwater flow equations in relatively simple domains, consisting of one or at most a few units having uniform hydraulic properties. Recently, attention has been shifting toward methods and solutions that would allow one to characterize subsurface heterogeneities in greater detail. On one hand, geostatistical inverse methods are being used to assess the spatial variability of parameters, such as permeability and porosity, on the basis of multiple cross‐hole pressure interference tests. On the other hand, analytical solutions are being developed to describe the mean and variance (first and second statistical moments) of flow to a well in a randomly heterogeneous medium. We explore numerically the feasibility of using a simple graphical approach (without numerical inversion) to estimate the geometric mean, integral scale, and variance of local log transmissivity on the basis of quasi steady state head data when a randomly heterogeneous confined aquifer is pumped at a constant rate. By local log transmissivity we mean a function varying randomly over horizontal distances that are small in comparison with a characteristic spacing between pumping and observation wells during a test. Experimental evidence and hydrogeologic scaling theory suggest that such a function would tend to exhibit an integral scale well below the maximum well spacing. This is in contrast to equivalent transmissivities derived from pumping tests by treating the aquifer as being locally uniform (on the scale of each test), which tend to exhibit regional‐scale spatial correlations. We show that whereas the mean and integral scale of local log transmissivity can be estimated reasonably well based on theoretical ensemble mean variations of head and drawdown with radial distance from a pumping well, estimating the log transmissivity variance is more difficult. We obtain reasonable estimates of the latter based on theoretical variation of the standard deviation of circumferentially averaged drawdown about its mean.

Regionalisation of catchment model parameters

We simulate the water balance dynamics of 308 catchments in Austria using a lumped conceptual model involving 11 calibration parameters. We calibrate and verify the model for two non-overlapping 11-year periods of daily runoff data. A comparison of the calibrated parameter values of the two periods suggests that all parameters are associated with some uncertainty although the degree of uncertainty differs between the parameters. The regional patterns of the calibrated parameters can be interpreted based on hydrological process reasoning indicating that they are able to represent the regional or large-scale differences in the hydrological conditions. Catchment attributes explain some of the spatial parameter variability with coefficients of determination of up to R 2=0.27, but usually the R 2 values are lower. Parameter uncertainty does not seem to cloud the relationship between calibrated parameters and catchment attributes to a significant extent as suggested by an optimised correlation analysis. The median Nash–Sutcliffe efficiencies of simulating streamflow decrease from 0.67 to 0.63 when moving from the calibration to the verification period. This is a small decrease, which suggests that problems with over-parameterisation of the model are unlikely. We then compare regionalisation methods for estimating the model parameters in ungauged catchments, in terms of the model performance. The best regionalisation methods are the use of the average parameters of immediate upstream and downstream (nested) neighbours and regionalisation by kriging. For the calibration period, the average decrease in the Nash–Sutcliffe model efficiency, as a result of the regionalisation, is 0.10 which is about twice the decrease of moving from the calibration to the verification period. The methods based on multiple regressions with catchment attributes perform significantly poorer. Apparently, spatial proximity is a better surrogate of unknown controls on runoff dynamics than catchment attributes.

Stochastic analyses of field‐scale pesticide leaching risk as influenced by spatial variability in physical and biochemical parameters

Field‐scale pesticide leaching risk assessments were performed by incorporating a numerical, one‐dimensional, water and pesticide transport and fate model into the two‐step stochastic modeling approach by Loll and Moldrup [1998]. The numerical model included first‐order pesticide degradation, linear equilibrium adsorption, and plant uptake of water and pesticide. Simazine was used as a model pesticide, and leaching risk was expressed as the cumulative mass fraction of applied pesticide leached below 100 cm after 1 year. Spatial variability in soil physical and biochemical data, as well as measured meteorological data from an average and a relatively wet year, was considered for two Danish field sites: (1) a coarse sandy soil, with relatively small variability in hydraulic properties, and (2) a sandy loam, with large variability in hydraulic properties. The two‐step stochastic modeling approach was used to investigate the relative impact of spatial variability in saturated hydraulic conductivity Ks, soil‐water retention through the Campbell [974] soil‐water retention parameter b, and pesticide sorption through the organic carbon content (OC). For the coarse sandy soil, field‐scale spatial variability in OC was the single most important parameter influencing leaching risk, whereas for the sandy loam, Ks was found more important than OC. The relative impact of field‐scale spatial variability in these parameters was found independent of the meteorological conditions, whereas the absolute level of leaching risk was highly dependent on the meteorological conditions. Assuming a linear dependency between pesticide half‐life and OC, a unified approach to modeling simultaneous field‐scale variability in biodegradation and adsorption was proposed. Leaching risk assessments based on this approach showed that the parts of the field with both low biological activity and low adsorption capacity contributed with a dramatic increase in leaching risk, and suggested that field‐scale spatial variability in biochemical processes can be of similar or larger importance than both hydraulic properties and meteorological conditions.

Simply Obtained Global Radiation, Soil Temperature and SoilMoisture in an Alley Cropping System in Semi-Arid Kenya

Global radiation, soil temperature and soil moisture data were obtained from a 4-6 year old Cassia siamea/maize (CM) alley cropping (or hedgerow intercropping) system, at a semi-arid site at Machakos, Kenya, in the late eighties. With the growing need to explore and manage variations in agro-ecosystems these results deserve new attention. They quantify, in a simple but detailed manner, the influence of hedgerows on the microclimate of their intercrop and for comparison provide a sole maize (SM) control. Due to inhomogeneity of Cassia and maize, as well as limited budgets, the sampling methodology and the choice of appropriate equipment, including the sensors, demanded special attention. The diurnal patterns of soil temperatures at 7.5 cm depth represented well the shading patterns of the hedgerows. This can be developed into an operational auxiliary methodology of integrated shade quantification. With proper precautions, the developed sampling methodologies showed appropriately the time integrated values of the three microclimatic parameters with enough detail to understand yield differences between treatments and between rows. This approach may therefore be recommended for on-farm quantification of even greater spatial variability of parameters. The limitations of the selected methods are highlighted. Experiences with some alternative methods are also discussed.