Bayesian estimation of the transmissivity spatial structure from pumping test data

Estimating the statistical parameters (mean, variance, and integral scale) that define the spatial structure of the transmissivity or hydraulic conductivity fields is a fundamental step for the accurate prediction of subsurface flow and contaminant transport. In practice, the determination of the spatial structure is a challenge because of spatial heterogeneity and data scarcity. In this paper, we describe a novel approach that uses time drawdown data from multiple pumping tests to determine the transmissivity statistical spatial structure. The method builds on the pumping test interpretation procedure of Copty et al. (2011) (Continuous Derivation method, CD), which uses the time-drawdown data and its time derivative to estimate apparent transmissivity values as a function of radial distance from the pumping well. A Bayesian approach is then used to infer the statistical parameters of the transmissivity field by combining prior information about the parameters and the likelihood function expressed in terms of radially-dependent apparent transmissivities determined from pumping tests. A major advantage of the proposed Bayesian approach is that the likelihood function is readily determined from randomly generated multiple realizations of the transmissivity field, without the need to solve the groundwater flow equation. Applying the method to synthetically-generated pumping test data, we demonstrate that, through a relatively simple procedure, information on the spatial structure of the transmissivity may be inferred from pumping tests data. It is also shown that the prior parameter distribution has a significant influence on the estimation procedure, given the non-uniqueness of the estimation procedure. Results also indicate that the reliability of the estimated transmissivity statistical parameters increases with the number of available pumping tests.Peer ReviewedPostprint (author's final draft

Vulnerability assessments of pesticide leaching to groundwater

Pesticides may have adverse environmental effects if they are transported to groundwater and surface waters. The vulnerability of water resources to contamination of pesticides must therefore be evaluated. Different stakeholders, with different objectives and requirements, are interested in such vulnerability assessments. Various assessment methods have been developed in the past. For example, the vulnerability of groundwater to pesticide leaching may be evaluated by indices and overlay-based methods, by statistical analyses of monitoring data, or by using process-based models of pesticide fate. No single tool or methodology is likely to be appropriate for all end-users and stakeholders, since their suitability depends on the available data and the specific goals of the assessment. The overall purpose of this thesis was to develop tools, based on different process-based models of pesticide leaching that may be used in groundwater vulnerability assessments. Four different tools have been developed for end-users with varying goals and interests: (i) a tool based on the attenuation factor implemented in a GIS, where vulnerability maps are generated for the islands of Hawaii (U.S.A.), (ii) a simulation tool based on the MACRO model developed to support decision-makers at local authorities to assess potential risks of leaching of pesticides to groundwater following normal usage in drinking water abstraction districts, (iii) linked models of the soil root zone and groundwater to investigate leaching of the pesticide mecoprop to shallow and deep groundwater in fractured till, and (iv) a meta-model of the pesticide fate model MACRO developed for 'worst-case' groundwater vulnerability assessments in southern Sweden. The strengths and weaknesses of the different approaches are discussed

Identification of Optimal Locations for Sampling Ground Water for Pesticides in the Mississippi Delta Region of Eastern Arkansas

Concerns about the presence of pesticides in the Mississippi River Valley alluvial aquifer in the Arkansas Delta have generated the need to develop a map of ground water vulnerability for this region comprised of approximately 10 million acres. Based on the availability of digital data and the scale of this study. we used a modified Pesticide DRASTIC model in a GRASS GIS environment to identify areas that were physically more sensitive to pesticide contamination than other areas within the Delta. Spatial distribution of pesticide loading was estimated from pesticide application rates in different crops and crop distribution map interpreted from satellite imagery. Relative ground water vulnerability index was expressed as a product of aquifer sensitivity index and pesticide loading index. The resulting map showing the spatial distribution of relative ground water vulnerability index values was intended for use in selecting optimal locations for sampling ground water for pesticides in the Arkansas Delta and for aid in implementing the Arkansas Agricultural Chemical Ground-Water Management Plan. The most sensitive areas in the Delta are distributed mostly along major streams where a combination of shallow depth to ground water, thin confining unit, permeable soils, and high recharge rate usually prevails. It is also in many of these areas where large acres of crops are grown, and pesticides are used. Consequently, many areas along major streams are also most vulnerable. These vulnerable areas may be targeted by planners and governmental agencies for further detailed evaluation. Uncertainties in the methodology and mapped input data, plus the dynamic nature of model factors, require continued and improved efforts in ground water vulnerability assessment for the Arkansas Delta

Relating in situ hydraulic conductivity, particle size and relative density of superficial deposits in a heterogeneous catchment

Estimating the permeability of superficial deposits is fundamental to many aspects of catchment science, but can be problematic where insufficient in situ measurements are available from pumping tests in piezometers. Consequently, common practice is to estimate permeability from the material description or, where available, particlesize distribution using a formula such as Hazen. In this study, we examine the relationships between particlesize, relativedensity and hydraulicconductivity in superficial deposits in Morayshire, Northern Scotland: a heterogeneous environment typical of many catchments subject to previous glaciations. The superficial deposits comprise glaciofluvial sands and gravels, glacial tills and moraines, raised marine sediments, and blown sands. Thirty-eight sites were investigated: hydraulicconductivity measurements were made using repeated Guelph permeameter measurements, cone resistance was measured in situ with a Panda dynamic cone penetrometer; material descriptions were made in accordance with BS5930:1999; and disturbed samples were taken for particlesize analysis. Overall hydraulicconductivity (K) varied from 0.001 m/d to >40 m/d; glacial till had the lowest K (median 0.027 m/d) and glacial moraine the highest K (median 30 m/d). However, within each geological unit there was great variability in measured hydraulicconductivity values. Multiple linear regression of the data indicated that log d10 and relativedensity (indicated by cone resistance or BS5930:1999 soil state description) were independent predictors of log K and together gave a relationship with an R2 of 0.80. Material description using the largest fraction (e.g. sand or gravel) had little predictive power. Therefore, in heterogeneous catchments, the permeability of superficial deposits is most strongly related to the finest fraction (d10) and relativedensity of the material. In situ Guelph permeameter measurements at outcrops with good geological characterisation provide an easy and reliable method of determining the permeability of particular units of superficial deposits

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

A double scale methodology to investigate flow in karst fractured media via numerical analysis. The Cassino plain case study (Central Apennine, Italy)

A methodology to evaluate the hydraulic conductivity of the karstmedia at a regional scale has been proposed, combining pumping tests and the hydrostructural approach, evaluating the hydraulic conductivity of fractured rocks at the block scale. Obtaining hydraulic conductivity values, calibrated at a regional scale, a numerical flow model of the Cassino area has been developed, to validate the methodology and investigate the ambiguity, related to a nonunique hydrogeological conceptual model. The Cassino plain is an intermontane basin with outstanding groundwater resources.The plain is surrounded by karst hydrostructures that feed the Gari Springs and Peccia Springs. Since the 1970s, the study area was the object of detailed investigations with an exceptional density of water-wells and piezometers, representing one of the most important karst study-sites in central-southern Italy. Application of the proposed methodology investigates the hydraulic conductivity tensor at local and regional scales, reawakening geological and hydrogeological issues of a crucial area and tackling the limits of the continuum modelling in karst medi

Delineating groundwater-surface water exchange flux using temperature-time series analysis methods

Groundwater-surface water interactions can play a crucial role in river-, riparian and wetland management. Their delineation and quantification at various spatial and temporal scales has become an important aspect in the study of contaminant transport and attenuation processes at the groundwater-surface water interface. One of the main parameters of interest is the groundwater-surface water exchange flux, which provides indications regarding stream-aquifer connectivity, the local flow regime as well as hydrogeological properties of the streambed. One of the methods to assess vertical exchange flux is through the analysis of temperature time-series. In this paper we delineate vertical exchange flux from temperature-time series collected at a Belgian River by comparing established numerical and analytical techniques with a novel approach. Results indicate a spatial variability of vertical fluxes over two orders of magnitude at the site