Source: Dissertation Abstracts International, Volume: 43-12, Section: B, page: 3896. / Thesis (Ph.D.)--The Florida State University, 1982.

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<p>The overall objective of this thesis is to characterize the space-time variability of snowpack physical properties at high spatial and temporal resolution for downscaling of remote-sensing products of snow cover, snow depth and snow water equivalent. The hypothesis is that the temporal evolution of the sub grid-scale statistical structure of relative permittivity fields and other snow properties can be related to the temporal evolution of the areal averages obtained from remote-sensing, thus enabling downscaling of snow water equivalent and snow depth even in the absence of ground-based measurements. For this purpose, research was conducted on ground-based measurements of subgrid-scale properties, and on the development and evaluation of a microwave simulation system consisting of coupled snow hydrology and radiative transfer models. </p><p>First, an L-band TX-RX wireless sensor to monitor snow accumulation and snow wetness was designed, fabricated, and tested under laboratory conditions. The sensor was designed to operate at 39 discrete frequencies (39 channels) in the 1.00-1.76-GHz frequency range (0.02-GHz increments). Full-system testing of the first-generation system was conducted using commercial attenuators up to 20.0 dB to test the prototypes against design specifications. It was determined that performance was nearly optimal in the 1-1.2-GHz range. Next, snow layers of varying snow wetness were physically modeled under controlled laboratory conditions. This was achieved by adding varying amounts of water to a layer of fixed porosity foam inside a rectangular tank placed above the transmitter. The attenuation and relative phase shift of the RF signal propagating through the experimental "snowpack" and through the laboratory "atmosphere" were subsequently analyzed as a function of volumetric water content equivalent to snow wetness. Under the space and geometry limitations of the laboratory setup, the data show that the single-frequency measurements exhibit high sensitivity for wetness values up to 24%, whereas multifrequency retrieval is necessary for higher liquid water contents. Measurements from a field deployment during snowfall in January 2009 are also presented. The results suggest that there is potential for using the RF sensor to measure cumulative snowfall for short-duration events.</p><p>Second, a land-surface hydrology model (LSHM) [Devonec 2002] with one-layer snowpack physics was coupled to a microwave emission model (MEMLS, [Wiesmann 1999], [Matzler 1999]) including and atmospheric attenuation correction algorithm. The objective is to develop a parsimonious and autonomous framework for monitoring snow water equivalent in remote regions where ancillary data and ground-based observations for model calibration and, or data assimilation are lacking. Two case-studies were conducted to evaluate the coupled hydrology-emission model in forward mode: 1) the intercomparison of a multi-year simulation of snowpack radio-brightness behavior at Valdai, Russia, against Scanning Multi-channel Microwave Radiometer (SMMR) observations at three frequencies (18, 21, and 37 GHz, V and H polarizations) for a six year period, 1978-1983; and 2) an intercomparison against Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) during February 2003 in Colorado as part of CLPX (Cold Land Processes Field Experiment). The results show that the model captures well the radiative behavior of the snowpack, especially for vertical polarization in the winter accumulation season January-March for all years of simulation and all cases. Large biases for the Valdai case study were identified for intermittent snowpack conditions at the beginning of the cold season (e.g. fall) which can be explained by uncertainty in fractional snow cover and spatial variability of skin liquid water content at the large spatial scale of SMMR products. Nevertheless, the modeling system uncertainty range remains below the known biases of SMMR products as compared to SSM/I [Derksen 2003]. Using meteorological data from NASA CLPX [Cline 2003], the simulated brightness temperatures agree well with SSM/I and AMSR-E during February, 2003. Overall, the coupled LSHM-MEMLS forward model also performs well at smaller scales and where more ancillary data are available, and its performance is consistent with that of more complex snow models. This research suggests there is therefore potential of this modeling framework model that does not require calibration for useful physically-based estimation of snow water equivalent from remote sensing observations over large areas at multiple spatial resolutions.</p><p>Third, the snow hydrology model was modified to include a multi-layer transient representation of the evolution of snowpack properties with time. The coupled multi-layer snow hydrology and emission model was implemented and tested independently for two very different climatic and physiographic regions (Valdai and CLPX2002-2003) for both wet and dry snow regimes over multiple years with good results both in terms of capturing the evolution of snowpack physical properties, and the radiometric signature consistent with SMMR, SSM/I and AMSR-E observations at 18-19, 22-23, and 36-37 GHz V and H polarizations. These applications show transferability of the modeling system, and its potential utility in large-scale retrieval over large areas with limited if any ground-based observations to constrain the model or for data-assimilation. Despite overall good skill as demonstrated by relatively low errors, one weakness was identified with respect to the simulation of the emission behavior of the snowpack, especially for horizontal polarization induced by liquid water in the snowpack, when ice layers (ice lenses) form due to freezing of liquid water either due to daytime melting, or due to rain-on-snow events. Furthermore, it was established that a more accurate estimation of snow density especially in the case of wet snow regimes would be important to improve skill for vertical polarization. Consequently, a multi-layer snow hydrology model (MLSHM) that can capture the events and snowpack gradients in water content and structure through accumulation, ripening and melting phases was developed and coupled to MEMLS. Significant differences between the simulations using the single and multilayer model formulations were found in the ripening and melting phases when wet snow regimes are more frequent. These differences result from differences in snow density, with the single-layer formulation exhibiting higher density (shallower snow depths) and faster melting rates. Whereas there are no significant changes in the microwave brightness temperatures in the vertical polarization from single to multilayer simulations, there is dramatic improvement in the results for horizontal polarization in Valdai, but not in the case of the more complex snow regimes in CLPX. Further work is required to improve parameterizations of snow density and snow structure including evolution of grain size distribution.</p><p>Overall error statistics and detailed analysis of physical behavior show that the coupled MLSHM-MEMLS is apt to be used in data-assimilation in snow retrieval.</p> / Dissertation

A problem common to many studies involving the use of unsaturated flow and chemical models is determining a representative expression for the value of unsaturated hydraulic conductivity K(ψ). A new steady-state inverse methodology called the multi-step steady-state outflow method (MSSOM) is presented here for the determination of unsaturated hydraulic conductivity. The method offers a practical alternative for the estimation of K(ψ) using either the exponential model, three-parameter model, or the van Genuchten formulations for K(ψ), a global-optimization simplex routine (MSSOM.EXE), and simple outflow data from a one-dimensional column experiment. The inverse technique was applied to a coarse sand and both the wetting an drying curves were well within the range of K(ψ) expected. Conductivity data from four other soils in the literature were then fitted using a curve fitting routine (RETC.F77) by van Genuchten, 1985 and compared to the inverse solution from the MSSOM model. The parameters for the K(ψ) expressions from both RETC and the MSSOM inverse model agreed well. Additional refinement of the multi-step steady-state outflow laboratory apparatus and the optimization program MSSOM.EXE are needed however to further improve the method.

The study of empirical teleconnections between annual and seasonal precipitation anomalies and different climate indices i.e. SOI, PDO, MEI, AAO, ONI, TWI, N3.4, TNI, and MJO, is an important approach to describe and analyze spatial and temporal precipitation variability. The objective of this study was to describe and analyze the main features of interannual and seasonal precipitation variability along Chile, and the influence of different ocean-atmospheric oscillations on secular long-term patterns. An exhaustive analysis through Empirical Orthogonal Functions (EOF) allowed determining the significant modes of annual and seasonal precipitation, and the main spatial patterns associated to them. Discrete Fourier Transform (DFT), the Singular Spectrum Analysis (SSA), and the Multichannel Singular Spectrum Analysis (M-SSA) were additionally used to determine the main frequencies and oscillatory components associated with leading climatic modes and precipitation patterns. The results agreed with those obtained by other authors and also when using different datasets. Despite advances on detailed annual and seasonal analysis that were carried out here, more detailed country-based studies are still required to develop a detailed understanding about climatic patterns along a country that is geographically very difficult to investigate.

Effective management of groundwater resources is at the forefront of environmental challenges confronting mankind in the 21st century. In many regions of the world, growing human populations coupled with decades of improper use and disposal of chemical contaminants have diminished the quantity and quality of this irreplaceable resource. It is projected that by the year 2025, thirty-five percent of the world population will experience chronic water shortages [Ahfeld et al, 2000]. As much of the world relies upon groundwater as its drinking water source (e.g. 51.7% of the United States population), optimal management will become increasingly important. Complicating the problem is that human use considerations must be balanced with environmental and economic concerns. Balancing multiple concerns such as these constitutes a multiobjective and conflict resolution problem, where tradeoffs among non-commensurable objectives must be identified to select the best compromise solution. In this research, a Computational Neural Network (CNN) methodology has been developed for identifying pumping policies for public supply wells that effectively balance risk of contamination with supply objectives. Utilizing simulation results from MODFLOW, monthly CNN's were developed to predict groundwater elevations at select locations for a hypothetical but realistic unconfined, heterogeneous aquifer under variable monthly pumping and recharge rates. The resulting CNN architecture, a simplified linear approximation to the finite-difference flow equations, was embedded into a linear optimization program, and an objective function that quantified both risk and supply was solved for using different weight preferences. The resulting Pareto frontier served as the basis for multiobjective and conflict resolution analyses. The CNN and decision-making methodologies were then applied to a real-world test case in Toms River, New Jersey, where contaminated public supply wells, a suspected cancer cluster, and few alternative water sources motivated the need for a formal and rigorous analysis that identified the best compromise solution. The new CNN methodology achieved a very high degree of accuracy in both simulation and optimization, and, once trained, is computationally more efficient than traditional methods. Perhaps most importantly, this research demonstrates the theoretical possibility of training a CNN with real-world data, allowing direct optimization of the actual groundwater system.

An automated system for distributed hydrologic modeling was developed for use with the Soil and Water Assessment Tool (SWAT: Arnold et al., 1996) and the Kinematic Runoff and Erosion Model (KINEROS; Smith et al., 1995). This suite of programs, the Automated Geospatial Watershed Assessment (AGWA) tool, was used to investigate the impacts of land cover change in watersheds within the Catskill/Delaware watershed complex in upstate New York and Upper San Pedro River basin in southeastern Arizona. As shown by classified remotely sensed imagery, these watersheds have undergone opposing land cover transitions over the past 25 years. SWAT simulations illustrated minor improvements in hydrologic response within the Castkill/Delaware watershed but a moderate increase in the San Pedro. A small watershed within the San Pedro was modeled using KINEROS to demonstrate localized impacts of land cover transitions. Fifty watersheds ranging in size from 5 to 100 km2 were prepared for input to the KINEROS model using a range of geometric complexities and rainfall events. A non-linear response to watershed complexity as a function of watershed scale and rainfall magnitude was observed. Small watersheds were more sensitive to landscape variability when small rainfall inputs were used, but the impact of spatial representation became insignificant when large rainfall inputs were used. Conversely, large watersheds were sensitive to spatial complexity for all rainfall data, although the relative effects were mitigated by increased rainfall. Remotely sensed imagery served as the primary mechanism for determining land cover within the study areas. A technique was created in which the errors from a misclassification error matrix were systematically introduced to a classified image to produce 100 realizations of land cover within the San Pedro basin. These realizations were used by AGWA to prepare input to KINEROS at a range of basin scales and rainfall inputs. The propagation of error and resultant uncertainty in simulation results was found to vary with watershed scale and rainfall input. Larger watersheds are more sensitive to misclassification error, while uncertainty in model output is inversely related to rainfall magnitude.

The adaptation of the electrical resistivity tomography (ERT) for monitoring of subsurface hydrological processes is the focus of this research. Specifically, the increase in the method's accuracy, expressed by its spatial and temporal resolution, is sought. A spatial sensitivity analysis of the ERT method is presented. This sensitivity analysis is conducted by a perturbation approach, and is making extensive use of the analytic element method (AEM) to compute potentials in the subsurface. Presented are sensitivity maps for individual typical and atypical arrays. Also presented are sensitivity maps for surveys comprised of a single array type and for mixed surveys, and guidelines for array selection for the detection of a localized target. Results indicate superiority of wide arrays over small arrays, and the relatively poor performance of the double dipole array type. Several optimality criteria are discussed for the selection of an optimal survey, including optimality of individual arrays to individual subsurface targets (locally optimal), and global optimality, achieved through the use of genetic algorithms. In both cases results show superiority of mixed surveys. The method presented here, for optimal ERT configuration, opens the way for implementation of the method for a wide variety of hydrological applications. In addition to the main focus of this dissertation, a complementary work was completed to extend the AEM to compute transient processes. This unique solution uses the Laplace transform to bring the flow equation to a linear, time independent form. The resultant modified Helmholtz equation is then solved using the AEM, and the result is numerically transformed to the time domain.

There are several sources of uncertainties in hydrologic modeling studies. Conventional deterministic modeling techniques typically ignore most of these uncertainties. However, there has been a growing need for better quantification of the accuracy and precision of hydrologic model predictions. Bayesian Recursive Estimation (BaRE) is an algorithm being developed towards considering these uncertainties for parameter estimation and prediction within an operational setting. This dissertation work evaluated and improved the current version of the algorithm. The methodology was improved using a progressive re-sampling of the Highest Probability Density (HPD) region of the parameter space, which concentrated the samples in the current HPD region while terminating computations in the nonproductive portions of the parameter space, rather than evaluating feasible parameter space based on the initial set of samples. The covariance structure of the well behaving parameter sets is used to generate new parameter sets, resulting in significant improvements compared to the original BaRE. Further, to reduce the "model/data overconfidence" problem, an entropy term and a data lack-of-confidence factor were introduced into the probability-updating rule. Comparison to batch calibration using the popular Shuffled Complex Evolution (SCE-UA) optimization method indicated that the improved recursive calibration technique is a powerful tool, especially useful where basins are recently gauged and hydrologic data are not well accumulated. The final method is also effective in tracing the temporal variations of parameters as a response to natural or human induced changes in the hydrologic system.

A higher-order theory is presented for steady state, mean uniform saturated flow and nonreactive solute transport in randomly homogeneous, statistically anisotropy natural log hydraulic conductivity fields Y. General integral expressions are derived for the spatial covariance of fluid velocity to second order in the variance σ² of Y in two and three dimensions. Analytical expressions are evaluated for integrals involving first-order (in σ) fluctuations in hydraulic head in two- and three-dimensional Y fields with exponential and Gaussian correlation functions. Integrals involving higher-order fluctuations of hydraulic head are evaluated numerically. Our results show that corrections involving higher-order head fluctuations are as important as those without them; neither should be disregarded. The ratio between second- and first-order variance approximations is larger in three- than in two-dimensions, larger for transverse than for longitudinal velocity, and increases with σ². The variance of longitudinal and transverse two- and three-dimensional velocity is larger in anisotropic than in isotropic fields due to higher-order effects. Second-order mean velocity is larger than first order in three dimensions and for most anisotropy ratios (except 1-6) in two dimensions. Transport requires approximations at flow and advection levels. Our results show that, in two dimensions, the combined effects of second-order correction to flow and to advection impact transport to a greater extent than does the sum of their individual effects. The choice of Y correlation function has greater effects on macrodispersivity than does second-order correction in flow. Published two-dimensional results of Monte Carlo simulations yield macrodispersivities that lie significantly below those predicted by first- and second-order theories. Considering that Monte Carlo simulations often suffer from sampling and computational errors, that standard perturbation approximations are theoretically valid only for σ² < 1, and that Corrsin's conjecture represents the leading term in a renormalization group perturbation which contains contributions from an infinite number of high-order terms, we find it difficult to tell which of these approximations is closest to representing transport in strongly heterogeneous media with σ² ≥ 1.