Hydraulic tomography (i.e., a sequential aquifer test) has recently been proposed as a method for characterizing aquifer heterogeneity. In this study a sequential inverse approach is developed to interpret results of hydraulic tomography. The approach uses an iterative geostatistical inverse method to yield the effective hydraulic conductivity of an aquifer, conditioned on each set of head/discharge data. To efficiently include all the head/discharge data sets, a sequential conditioning method is employed. Two-dimensional numerical experiments were conducted to investigate the optimal sampling scheme for the hydraulic tomography. The effects of measurement errors and uncertainties in statistical parameters required by the inverse model were also investigated. The robustness of this inverse approach was demonstrated through its application to a hypothetical, three-dimensional, heterogeneous aquifer. Two sandbox experiments were conducted to evaluate the performance of the sequential geostatistical inverse approach under realistic conditions. One sandbox was packed with layered sands to represent a stratified aquifer while the other with discontinuous sand bodies of different shapes and sizes to represent a more complex and realistic heterogeneous aquifer. The tomography was found ineffective if abundant head measurements were collected at closely spaced intervals in a highly stratified aquifer. While it was found beneficial when head measurements were limited and the geological structure was discontinuous. The sequential inverse approach for hydraulic tomography was extended for electrical resistivity tomography. Numerical experiments were conducted to demonstrate the robustness of this approach for delineating the resistivity distribution in the subsurface and to investigate effectiveness of different sampling arrays of the ERT: the surface, the down-hole, and the combination of the surface and down-hole array. Orientation of bedding was found to dictate the effectiveness of the ERT layout. Samples were collected to quantify spatial variability of the resistivity-moisture relationship in the field. Numerical experiments then illustrated how the spatially varying relationship exacerbated the level of uncertainty in the interpretation of change of moisture content based on the estimated change in resistivity. A sequential inverse approach was then developed to estimate water content with less uncertainty by considering the spatial variability of the resistivity-moisture relationship and incorporating point moisture measurements and ERT data sets.

The quality of MM5 ensembles is evaluated for short-range probabilistic quantitative precipitation forecasts over Arizona during the Southwest monsoon. The sensitivity of different ensemble constructs is examined with respect to analysis uncertainty, model parameterization uncertainty, and a combination of both. Model uncertainty is addressed through different cumulus and planetary boundary layer parameterizations, and through stochastic forcing representative of a component of subgrid-scale uncertainty. A first-order autoregression model adds a stochastic perturbation to the Kain-Fritsch cumulus scheme and MRF planetary boundary layer scheme. A sensitivity study is also conducted to determine the MM5 planetary boundary layer parameterizations capable of simulating the structure of the pre-convective, monsoon atmospheric boundary layer. The results indicate that ensemble precipitation forecasts are skillful and may assist operational weather forecasters during the monsoon. The most skillful ensembles contain both analysis perturbations and mixed-model physics. The Blackadar or MRF planetary boundary layer schemes are recommended for MM5 simulations or forecasts of the Southwest monsoon.

This study created a new tectonic model for the Arctic Alaska terrane (AAT) by connecting attributes interpreted from surface and subsurface maps. Lineaments that cross the Brooks Range and North Slope proclaim the presence of basement fault blocks trending to the northeast that locally are aligned with streams, coast and lake shorelines, submarine canyons, and periglacial features. These landforms and anomalies reflect upward propagation of long-lived transcurrent and rift fault fabrics. Facies mapping and analysis of heat-flow effects on permafrost, and data from aeromagnetic, gravity and reflection seismic surveys, support the correlation of basement faulting with geomorphic patterns. The conjugate pattern of fault blocks, seen across Paleozoic- and Mesozoic-age passive margin sequences, resembles a piano keyboard and was inherited from older rift margin and transcurrent-transfer faults. Seismic data and North Slope oil-reservoir characteristics reveal complex fault-block boundaries, and common fault reactivation and structural inversion. The rigid North American craton in the Yukon Territory directs deformation westward leading to continued crustal indention, migration of basement blocks, and thrusting of cover rocks north of the Arctic oroclinal bend. Differential south-vergent underthrusting and uplift of the basement blocks of the North Slope plate has episodically segmented and partitioned strain across the overlying weaker north-vergent cover rocks of the North Alaskan plate. These tectonic controls have influenced the structural and geomorphic evolution of the North Slope-Brooks Range foothills region, including the formation of oil and gas reservoirs and mineral deposits.

The goal of this study is to provide an improved, high resolution, regional diagnosis of three important surface variables on the land surface energy and water balance, namely the downward short-wave and downward long-wave surface radiation fluxes, and precipitation. Cloud cover is a key parameter linking and controlling these three terms. An automatic procedure was developed to derive high-resolution (4 km x 4 km) fields of fractional cloud cover from visible band, (GOES series) geostationary satellite data using a novel tracking procedure to determine the clear-sky composite image. In our initial data assimilation studies, the surface short-wave radiation fluxes calculated by RAMS were simply replaced by the equivalent estimated values obtained by applying this high-resolution satellite-derived cloud cover in the UMD GEWEX/SRB model. However, this initial study revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming long-wave radiation and precipitation fields, because modeled cloud cover remained unchanged and, consequently, these other surface fields retained their low, clear-sky values. It was recognized that the UMD GEWEX/SRB model provides an important relationship between cloud albedo, cloud optical depth and cloud water/ice. Thus, exploration was made of feasibility of directly assimilating vertically integrated cloud water/ice fields to update modeled cloud cover. This approach will not only enhance the realism of radiation scheme in RAMS, but it may also dramatically increase the model's capability to predict the location of precipitation, thus enhancing the ability of such mesoscale modeling systems to make accurate short-term forecasts of precipitation. This, in turn, would benefit flood forecasting as an associate hydrologic response. In the method adopted, the assimilated image takes the horizontal distribution of cloud from the satellite image but it retains a vertical distribution which is the area-average simulated by RAMS across the modeled domain in the time step immediately prior to cloud assimilation. Cloud assimilation is made every minute, with linear interpolation applied to derive cloud images for each minute between two GOES samples. Comparisons were made between modeled and observed data taken from the AZMET weather station network for model runs with and without cloud assimilation to demonstrate the improvement in RAMS' ability to describe surface radiation and precipitation fields. Cloud assimilation was found to substantially improve the RAMS model's ability to capture both the temporal and spatial variations in surface fields associated with observed cloud cover. The sensitivity of these comparisons to model initiation was explored by making five ensemble runs starting from different initiation. In general, RAMS with cloud assimilation technique is not sensitive to realistic perturbation of initial conditions.

In the 1980s, USAID (US Agency for International Development) funded an environmental assessment of dams on the Gambia River, which determined that construction of the Balingho anti-salinity barrage would result in adverse unmitigative environmental and social consequences. Attempts by host country politicians, USAID and UNDP (United Nations Development Programme) to discredit this process made it necessary to take the matter to the Natural Resource Defense Council. A case study of the events surrounding these dams and their potential construction illustrates the ‘big dam’ paradigm and its potential harm to people, their livelihoods and the environment in Sub-Saharan Africa.

There is evidence to suggest that the air-water interface serves as an important retention domain for volatile organic compounds (VOCs) in vadose-zone soil systems. Gas-phase solute transport experiments were conducted to evaluate the influence of air-water interfacial adsorption on trichloroethene transport and retention. Mechanical mixing and diffusion were observed to contribute significantly to the transport of gases and vapors in unsaturated soils. The relative contribution of individual dispersion processes was governed largely by the differences in their diffusion coefficients, while changes in gas-phase tortuosity and linear velocity due to soil-water content changes represented secondary effects. Difluoromethane (DFM) was shown to hold promise as a reactive tracer for the in-situ measurement of soil-water content, as there was shown to be a linear relationship between DFM-estimated and measured soil-water contents. Heptane was shown here to exhibit nonideal tracer behavior that complicate its use in estimating air-water interfacial areas. Conversely, relatively ideal interfacial tracer properties were exhibited by decane. In excess of 90% of the decane retardation factor was contributed by adsorption at the air-water interface, rendering other forms of retention entirely secondary. Decane retardation factors were in an appropriate range for soil-water contents greater than ∼2.5%. Specific air-water interfacial areas estimated from decane retention data appear to be reasonable, based on comparison with the measured N₂√BET specific surface area of the porous media and comparison with literature data. The retention of TCE vapor in unsaturated soil systems to be influenced by several processes, including sorption to the solid surfaces, dissolution into bulk soil-water, and adsorption at the air-water interface. The retention of TCE in the system under various conditions was not predicted well by the traditional retardation equation. However, use of DFM (water-partitioning tracer) data and decane (interfacial tracer) data were observed to further improve agreement between predicted and experimental TCE retardation factors. Interfacial processes were shown to influence TCE retention most significantly at soil-water contents less than 5%, contributing 87% of the total measured retardation factor at 2% soil-water content.

We consider the effect of randomness of hydraulic conductivities K(x) on numerical predictions, without resorting to either Monte Carlo simulation, of transient flow in bounded domains driven by random source, initial and boundary terms. Our aim is to allow optimum unbiased prediction of hydraulic heads h(x, t) and fluxes q(x,t) by means of their respective ensemble moments, <h( x,t)>c and < q(x,t)>c, conditioned on measurements of K(x). These predictors have been shown by Tartakovsky and Neuman (1998) to satisfy exactly a space-time nonlocal (integro-differential) conditional mean flow equation in which < q(x,t)>c is generally non-Darcian. Exact nonlocal equations have been obtained for second conditional moments of head and flux that serve as measures of predictive uncertainty. The authors developed recursive closure approximations for the first and second conditional moment equations through expansion in powers of a small parameter σᵧ , which represents the standard estimation error of ln K(x). The authors explored the possibility of localizing the exact moment equations in real, Laplace- and/or infinite Fourier-transformed domains. In this paper we show how to solve recursive closure approximations of nonlocal first and second conditional moment equations numerically, to first order in σ²ᵧ, in a bounded two-dimensional domain. Our solution is based on Laplace transformation of the moment equations, parallel finite element solution in the complex Laplace domain, and numerical inversion of the solution from the Laplace to the real time domain. We present a detailed comparison between numerical solutions of nonlocal and localized moment equations, and Monte Carlo simulations, under superimposed mean-uniform and convergent flow regimes in two dimensions. The results are shown to compare very well for variances σ²ᵧ as large as 4. The degree to which parallelization enhances computational efficiency is explored.

Drought characterization and forecasting are extremely important in the planning and management of water resources systems. In a transboundary region, especially, sustainable water use and water rights are major issues among communities and countries during droughts. For instance, the drought of the 1990s in the Conchos River Basin in Mexico resulted in the severe reduction of its inflows into the Bravo/Grande River. Droughts in the Conchos River Basin affect many communities in the Lower Bravo/Grande River Basin, and become a controversial issue between the United States and Mexico. This research focuses on the application of nonparametric methods to characterize and forecast droughts using a drought index like the Palmer Drought Severity Index. Nonparametric methods allow more flexible approaches to hydrologic problems in practice by approximating a function of interest. Based on a nonparametric probability density function estimator, comprehensive approaches for the evaluation of drought characteristics at a site and over a region are proposed in this study. Using a kernel density estimator, a nonparametric methodology is proposed for the synthetic generation of hydrologic time series. Based on the synthetic data, a nonparametric approach is introduced for estimating the bivariate characteristics of drought. A kernel density estimator is useful in the estimation of the probability density function and the cumulative distribution function in two dimensions of drought properties. A methodology for the regional characterization of droughts is developed using the point drought properties. The nonlinear and nonparametric features of artificial neural networks provide a useful framework in forecasting hydrologic time series. A conjunction model is presented in this study in order to improve forecast accuracy for time series of droughts. The conjunction model is a hybrid neural network model combined with dyadic wavelet transforms. The overall results presented in this study indicate that the proposed methods are useful for identification of droughts, evaluation of drought characteristics, and prediction of drought occurrence. The appropriate evaluation and accurate prediction of droughts allow water resources decision makers to prepare efficient management plans and proactive mitigation programs which can reduce drought-related social, environmental, and economic impact significantly.

Annual, seasonal, and daily discharge patterns determine many of the physical and biological properties of a stream. Natural short- and long-term variation of streamflow is part of the normal processes of a river or stream whereas artificial short- and long-term fluctuations can disrupt the natural processes of a river. It is critical to recognize and identify such artificial fluctuations and disturbances to have a more complete understanding of river systems. This understanding can be used to modify current management efforts to achieve more natural flow regimes. A new procedure using dual-timescale graphs is presented to visualize streamflow characteristics and to measure temporal change objectively. Theoretical development, procedural guidelines, and interpretation of results are included in the development of this new approach. The raster-based method is applied to two large river systems in the western United States. Data from twelve U.S. Geological Survey (USGS) streamflow stations within the middle and upper Snake River Basin and upper Colorado River Basin were analyzed using a dual-timescale raster-grid to identify flow signatures and disturbances. Patch-analysis and pattern quantification techniques used in landscape ecology were applied to dual-timescale raster-based hydrographs. Both river basins included gaging stations where minimal human-caused disturbances have taken place within the respective watershed. These stations function as control sites for interpretation of grid-correlograms and patch-analysis results.

This research presents an integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS. The integrated simulation system consists of three subsystems: a stochastic space-time rainfall model, a geographical information system (GIS), and a distributed physically-based hydrologic model. The developed stochastic space-time rainfall model is capable of estimating the storm movement and simulating a random rainfall field over a study area, based on the measurement from three raingauges. An optimization-based lag-k correlation method was developed to estimate the storm movement, and a stochastic model was developed to simulate the rainfall field. A GIS tool, ARC/INFO, was integrated into this simulation system. GIS has been applied to automatically extract the spatially distributed parameters for hydrologic modeling. Digital elevation modeling techniques were used to process a high resolution digital map. A distributed physically-based hydrologic model, operated in HEC-1, simulated the stochastic, distributed, interrelated hydrological processes. The Green-Ampt equation is used for modeling the infiltration process, kinematic wave approximation for infiltration-excess overland flow, and the diffusion wave model for the unsteady channel flow. Two small nested experimental watersheds in southern Arizona were chosen as the study area where three raingauges are located. Using five recorded storm events, a series of simulations were performed under a variety of conditions. The simulation results show the model performs very well, by comparing the simulated runoff peak flow and runoff depth with the measured ones, and evaluated by the model efficiency. Both model structure and model parameter uncertainties were investigated in the sensitivity analysis. The statistical tests for the simulation results show that it is important to model stochastic rainfall with storm movement, which caused a significant change in runoff peak flow and runoff depth from that where the input is only one gage data. The sensitivity of runoff to roughness factor N and hydraulic conductivity Ks were intensively investigated. The research demonstrated this integrated system presents an improved simulation environment for the distributed hydrology.