Ayman Alzraiee

Modeling Subsurface Heterogeneity of Irrigated and Drained Fields. II: Multivariate Stochastic Analysis of Root-Zone Hydrosalinity and Crop Yield

AbstractSpatial heterogeneity and measurement error in parameters representing flow and salt transport properties in irrigated and drained fields make numerical prediction of soil hydrosalinity conditions, crop yield response, and other variables prone to uncertainty. This paper presents a method to account for uncertainty of correlated regionalized parameters in simulating key performance variables using a three-dimensional (3D) flow and transport model, the Colorado State University Irrigation and Drainage (CSUID) model, coupled with multivariate Monte Carlo simulation. Sequential indicator simulation is used to generate 3D correlated realizations for hydraulic conductivity, porosity, residual water content, van Genuchten parameters, and dispersivity. Other semiempirical parameters that control crop water uptake, irrigation efficiency, and subsurface drainage conductance also are randomized. The generated ensembles for each of the considered parameters are processed with the CSUID model to obtain the sp...

Using Cluster Analysis of Hydraulic Conductivity Realizations to Reduce Computational Time for Monte Carlo Simulations

AbstractDespite the conceptual simplicity of Monte Carlo-simulation methods in assessing uncertainty in hydrogeological systems, their use is limited by expensive computational requirements in terms of the large number of realizations that must be processed. Cluster analysis was applied in this paper to reduce the number of realizations to be processed by flow simulators while efficiently approximating flow-response statistics. Different clustering techniques were used to partition the ensemble of realizations into a few clusters that were significantly different from each other and had maximum intracluster similarity. The clustering step was achieved by using different similarity metrics. Then a subsample of the realizations was collected to represent the uncertainty in the whole ensemble. Two methods for collecting the subsample were investigated: the stratified sampling and centroid-based sampling. The performance of different clustering and sampling techniques was tested by evaluating the mismatch bet...

Assimilation of historical head data to estimate spatial distributions of stream bed and aquifer hydraulic conductivity fields

Management of water resources in alluvial aquifers relies mainly on understanding interactions between hydraulically connected streams and aquifers. Numerical models that simulate this interaction often are used as decision support tools in water resource management. However, the accuracy of numerical predictions relies heavily on the unknown system parameters (i.e. stream bed conductivity and aquifer hydraulic conductivity) which are spatially heterogeneous and difficult to measure directly. This paper employs an Ensemble Smoother to invert groundwater level measurements to jointly estimate spatially-varying streambed and alluvial aquifer hydraulic conductivity along a 35.6 km segment of the South Platte River in northeastern Colorado. The accuracy of the inversion procedure is evaluated using a synthetic experiment and historical groundwater level measurements, with the latter constituting the novelty of this study in the inversion and validation of high resolution fields of streambed and aquifer conductivities. Results show that the estimated streambed conductivity field and aquifer conductivity field produce an acceptable agreement between observed and simulated groundwater levels and stream flow rates. The estimated parameter fields are also used to simulate the spatially varying flow exchange between the alluvial aquifer and the stream, which exhibit high spatial variability along the river reach with a maximum average monthly aquifer gain of about 2.3 m3/day and a maximum average monthly aquifer loss of 2.8 m3/day, per unit area of streambed (m2). These results demonstrate that data assimilation inversion provides a reliable and computationally affordable tool to estimate the spatial variability of streambed and aquifer conductivities at high resolution in real-world systems.

Geomechanical Characterization of Storage Reservoirs by Assimilation of Surface Displacements

Gas injection into the subsurface is becoming increasingly popular worldwide in connection with Underground Gas Storage (UGS) and CO2 sequestration (CCS) projects. Depleted oil/gas fields or saline aquifers are strategically used to cope with the growing demand of energy and the planned reduction of the greenhouse efflux into the atmosphere. Due to the pressure increase caused by gas injection, porous rock formations expand and land surface rises. The surface displacements, when accurately measured for example by SAR interferometry, can be effectively used to evaluate the petrophysical and geomechanical properties of the injected formation. In this study, an Ensemble Smoother (ES) data assimilation technique is applied to reduce the uncertainty on the constitutive parameters characterizing the geomechanical model of a UGS field situated in the Po River basin, north of Italy. The assimilation has been implemented using vertical and East-West displacements measured by an advanced satellite technology based on Radarsat scenes collected between 2003 and 2008.