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The aim of this work is to understand the exchange of water between the Serra Geral aquifer system (SGAS) and Guarani aquifer system (GAS). The objectives are two-fold. First, introduce the capability of the modified self-organizing maps (MSOM) as an unbiased nonlinear approach to estimate missing values of hydrochemistry and hydraulic transmissivity associated with the SGAS, a transboundary groundwater system spanning parts of four South American countries. Second, identify areas with potential connectivity of the SGAS with the GAS based on analysis of the spatial variability of key elements and comparison with current conceptual models of hydraulic connectivity. The MSOM is employed to calculate correlations (trends) between 27 variables from 1,132 wells. Hydraulic transmissivity is calculated from specific capacity values from well-pump tests in 157 locations. Hydrochemical facies estimates appear unbiased and consistent with current conceptual-connectivity models indicating that vertical fluxes from GAS are influenced by geological structure. The MSOM provides additional spatial estimates revealing new areas with likely connections between the two aquifer systems.

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibiraci, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively...

The objectives of this research were to determine the role of preferential flow paths in the transport of atrazine (2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine) and dicamba (3-6-dichloro-2-methoxybenzoic acid) through silt and loam soils overlying the High Plains aquifer in Nebraska. In a previous study, 3 of 6 study areas demonstrated high percentages of macropores; those three areas were used in this study for analysis of chemical transport. As a subsequent part of the study, 12 intact soil cores (30-cm diameter by 40-cm height), were excavated sequentially, two from each of the following depths: 0-40cm and 40-80cm. These cores were used to study preferential flow characteristics using dye staining and to determine hydraulic properties. Two undisturbed experimental field plots, each with a 3-m2 surface area, were installed in three study areas in Nebraska. Each was instrumented with suction lysimeters and tensiometers at depths of 10cm to 80cm in 10-cm increments. Additionally, each plot was planted with corn (Zea mays). A neutron probe access tube was installed in each plot to determine soil water content at 15-cm intervals. All plots were enclosed with a raised frame (of 8-cm height) to prevent surface runoff. All suction lysimeters were purged monthly for three months and were sampled immediately prior to pre-plant herbicide application to obtain background chemical concentrations. Atrazine and dicamba moved rapidly through the soil, but only after a heavy rainfall event, probably owing to the presence of preferential flow paths and lack of microbial degradation in these soil areas. Staining of laboratory cores showed a positive correlation between the percent area stained by depth and the subsequent breakthrough of Br- in the laboratory and leaching of field-applied herbicides owing to large rainfall events. Suction lysimeter samples in the field showed increases in concentrations of herbicides at depths where laboratory data indicated greater percentages of what appeared to be preferential flow paths. Concentrations of atrazine and dicamba exceeding 0.30 and 0.05µg m1-1 were observed at depths of 10-30cm and 50-70cm after two months following heavy rainfall events. It appears from the laboratory experiment that preferential flow paths were a significant factor in transport of atrazine and dicamba. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government or GNS.

A hybrid modeling approach is proposed for near real-time three-dimensional (3D) mapping of surficial aquifers. First, airborne frequency-domain electromagnetic (FDEM) measurements are numerically inverted to obtain subsurface resistivities. Second, a machine-learning (ML) algorithm is trained using the FDEM measurements and inverted resistivity profiles, and borehole geophysical and hydrogeologic data. Third, the trained ML algorithm is used together with independent FDEM measurements to map the spatial distribution of the aquifer system. Efficacy of the hybrid approach is demonstrated for mapping a heterogeneous surficial aquifer and confining unit in northwestern Nebraska, USA. For this case, independent performance testing reveals that aquifer mapping is unbiased with a strong correlation (0.94) among numerically inverted and ML-estimated binary (clay-silt or sand-gravel) layer resistivities (5–20 ohm-m or 21–5,000 ohm-m), and an intermediate correlation (0.74) for heterogeneous (clay, silt, sand, gravel) layer resistivities (5–5,000 ohm-m). Reduced correlation for the heterogeneous model is attributed to over-estimating the under-sampled high-resistivity gravels (about 0.5 % of the training data), and when removed the correlation increases (0.87). Independent analysis of the numerically inverted and ML-estimated resistivities finds that the hybrid procedure preserves both univariate and spatial statistics for each layer. Following training, the algorithms can map 3D surficial aquifers as fast as leveled FDEM measurements are presented to the ML network.

A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.

Spatial analysis of soil samples is often times not possible when measurements are limited in number or clustered. To obviate potential problems, we propose a new approach based on the self-organizing map (SOM) technique. This approach exploits underlying nonlinear relation of the steady-state geomorphic concave–convex nature of hillslopes (from hilltop to bottom of the valley) to spatially limited soil textural data. The topographic features are extracted from Shuttle Radar Topographic Mission elevation data; whereas soil textural (clay, silt, and sand) and hydraulic data were collected in 29 spatially random locations (50 to 75 cm depth). In contrast to traditional principal component analysis, the SOM identifies relations among relief features, such as, slope, horizontal curvature and vertical curvature. Stochastic cross-validation indicates that the SOM is unbiased and provides a way to measure the magnitude of prediction uncertainty for all variables. The SOM cross-component plots of the soil texture reveals higher clay proportions at concave areas with convergent hydrological flux and lower proportions for convex areas with divergent flux. The sand ratio has an opposite pattern with higher values near the ridge and lower values near the valley. Silt has a trend similar to sand, although less pronounced. The relation between soil texture and concave–convex hillslope features reveals that subsurface weathering and transport is an important process that changed from loss-to-gain at the rectilinear hillslope point. These results illustrate that the SOM can be used to capture and predict nonlinear hillslope relations among relief, soil texture, and hydraulic conductivity data.

A regionalization procedure is presented and used to predict probable flooding in four ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. The flood-prediction problem is sequentially solved for two regions: upstream mountains and downstream alluvial plains. In the upstream mountains, a set of rainfall-runoff parameter values and recurrent peak-flow discharge hydrographs are simultaneously estimated for 20 tributary-basin models. Application of dissimilarity equations among tributary basins (soft prior information) permitted development of a parsimonious parameter structure subject to information content in the recurrent peak-flow discharge values derived using regression equations based on measurements recorded outside the ungauged study basins. The estimated joint set of parameter values formed the basis from which probable minimum and maximum peak-flow discharge limits were then estimated revealing that prediction uncertainty increases with basin size. In the ...

A regularized joint inverse procedure is presented and used to estimate the magnitude of extreme rainfall events in ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. Since streamflow measurements reflect temporal and spatial rainfall information, peak-flow discharge is hypothesized to represent a similarity measure suitable for regionalization. To test this hypothesis, peak-flow discharge values determined from streamflow recurrence information (10-year, 25-year, and 100-year) collected outside the study basins are used to develop regional (country-wide) regression equations. Peak-flow discharge derived from these equations together with preferred spatial parameter relations as soft prior information are used to constrain the simultaneous calibration of 20 tributary basin models. The nonlinear range of uncertainty in estimated parameter values (1 curve number and 3 recurrent rainfall amounts for each model) is determined using an inverse calibration-constrained Monte Carlo approach. Cumulative probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase in magnitude and range among basins with increasing return interval. Comparison of the estimated median rainfall amounts for all return periods were reasonable but larger (3.2–26%) than rainfall estimates computed using the frequency-duration (traditional) approach and individual rain gauge data. The observed 25-year recurrence rainfall amount at La Hachadura in the Paz River basin during Hurricane Mitch (1998) is similar in value to, but outside and slightly less than, the estimated rainfall confidence limits. The similarity in joint inverse and traditionally computed rainfall events, however, suggests that the rainfall observation may likely be due to under-catch and not model bias.

Artigos relacionados com as alterações climáticas e seus possíveis efeitos nos recursos hídricos, nomeadamente os recursos hídricos subterrâneos / Papers related with climatic changes and its possible effects in water resources, namelly in groundwater.

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

Mauritania anticipates an increase in mining activities throughout the country and into the foreseeable future. Because mining-induced changes in the landscape are likely to affect their limited ground-water resources and sensitive aquatic ecosystems, a water-quality assessment program was designed for Mauritania that is based on a nationally consistent environmental stratification framework. The primary objectives of this program are to ensure that the environmental monitoring systems can quantify near real-time changes in surface-water chemistry at a local scale, and quantify intermediateto long-term changes in groundwater and aquatic ecosystems over multiple scales.

The U.S. Geological Survey (USGS), in cooperation with the Northern Colorado Water Conservancy District (Northern) and Denver Water (Denver), began a study in 2001 of the amount of water available for storage in the proposed Sulphur Gulch reservoir and the effects that reservoir operations might have on dissolved-solids concentrations (herein referred to as “salinity”) in the Colorado River under four reservoir-operating scenarios. The proposed 16,000-acrefoot reservoir would be located about 25 miles east of Grand Junction, Colo., on a tributary of the Colorado River that drains the Sulphur Gulch watershed between De Beque and Cameo, Colo. (fig. 1). Denver and Northern are considering building the reservoir to augment streamflows by as much as 300 cubic feet per second (ft/s) during critical low-flow conditions in the fall, and possibly also during the peak spring runoff, to increase flows for endangered fish as recommended by the U.S. Fish and Wildlife Service (1999). Water would be pumped from the Colorado River, when available, to fill the reservoir at a rate of 150 ft/s, during times that releases are not occurring. This fact sheet summarizes a detailed report of the study (Friedel, 2005), including an overview of the modeling approach and simulated effects of reservoir operations on the amount of water available for storage in the proposed Sulphur Gulch reservoir and salinity of the Colorado River at the Government Highline Canal and Grand Valley Irrigation Canal diversions. In the study, a stochastic model was used that takes into account the random and uncertain nature of the quantity and salinity of hydrologic variables (such as streamflow, runoff, evaporation, canal diversions, and reservoir storage) and provides results as a probable range of values for the hydrologic variables and salinity that would result from the reservoir operations. The model provides an understanding of the likely effects of operation of the reservoir that accounts for the day-to-day and year-to-year uncertainty and variability in Colorado River streamflow and salinity, quantity and salinity of runoff from the Sulphur Gulch watershed, and evaporation from the proposed reservoir.

The upper Illinois River Basin (UIRB) is the 10,949-square-mile drainage area upstream from Ottawa, Illinois on the Illinois River and is one of the U.S. Geological Survey’s National WaterQuality Assessment (NAWQA) program study units. To assist in the interpretation of groundwater data that will be collected during the course of the UIRB study, the study-unit team designed a spatial model to describe recharge potential of surficial and shallow bedrock aquifers. The following factors, identified as having an effect on recharge potential, were incorporated into the model: land use, soil permeability, type and thickness of surficial deposits, and uppermost bedrock geology. Other models designed to simulate recharge potential and the potential for contamination that were examined during the preparation of this model included factors similar to those included in this model, with the exception of land use. Land use and changes in land use over time, however, can affect recharge potential. The UIRB model was used to simulate recharge potential with and without incorporating land use. A comparison of the simulation results showed that recharge potential was overestimated in some areas and underestimated in other areas when land use was not included in the model. Comparisons of simulations that used 1970 and estimated 1990 land use showed changes in recharge potential over time. INTRODUCTION The National Water-Quality Assessment (NAWQA) program of the U.S. Geological Survey was designed to provide a national view of the status and trends of the Nation’s water resources (Hirsch and others, 1988). This national design facilitates a comparison of water-quality conditions across the country and provides consistent monitoring of water-quality conditions over time. The NAWQA program utilizes an interdisciplinary approach, which integrates groundand surface-water hydrology and biology, to study basin ecosystems. The NAWQA program is designed around approximately 60 major surface-water drainage basins, called study units. These study units include about one-half the area of the conterminous United States and supply water to approximately 65 percent of the population that relies on public-water supply (Gilliom and others, 1995). The upper Illinois River Basin (UIRB) is one such study unit in the NAWQA program (Friedel, 1998). The UIRB covers 10,949 mi 2 upstream from Ottawa, Illinois, on the Illinois River in parts of northeastern Illinois, northwestern Indiana, and southeastern Wisconsin. Some of the major water-quality issues in the UIRB are related to urban and agricultural land use: municipal and industrial wastewater releases, urban and agricultural runoff, and atmospheric deposition of pesticides and trace metals (Friedel, 1998). In 1990, most land use in the basin was agricultural (75 percent of the area), followed by urban (17 percent of the area) (Hitt, 1994; Hitt, 1992; Arnold and others, 1999). The human populations and activities within these two principal land-use areas make large demands on the water resources of the basin. Chicago is the largest urban area in the UIRB. In the northern part of the basin, particularly along the Fox and Des Plaines Rivers, suburban expansion