A METHOD FOR OBTAINING SLOPE INFORMATION FOR HYDROLOGIC MODELS1

: A method is described for obtaining surface slope information for analysis with other land resource and water quality data in hydrologic models of nonpoint sources of water pollution. The method described requires a point sampling scheme, topographic maps, and a coordinate digitizer. Sample point elevation, slope direction, and slope magnitude are calculated from locations of the sample point and the nearest upper and lower contour lines. Details of the data collection methodology and associated problems are discussed.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.     

Assessment of groundwater quality by means of self-organizing maps: application in a semiarid area

The Kohonen neural network was applied to hydrochemical data from the Detritic Aquifer of the Lower Andarax, situated in a semiarid zone in the southeast of Spain. An activation map was obtained for each of the sampling points, in which the spatial distribution of the activated neurons indicated different water qualities. To extract the information contained in the activation maps, they were divided into nine quadrats. Cartesian coordinates were assigned to each quadrant (x, y), and for each sampling point, three derived variables were selected, which were assigned the values x and y of the corresponding quadrat. A classification was defined based on this simple matrix system which allows an easy and rapid means of evaluating the water quality. This assessment highlights the various processes that affect groundwater quality. The method generates output that is easier to interpret than from traditional statistical methods. The information is extracted from the activation maps without significant loss of information. The method is proposed for assessing water quality in hydrogeochemically complex areas, where large numbers of observations are made.     

ESTIMATING TRANSMISSIVITY AND HYDRAULIC CONDUCTIVITY OF CHICOT AQUIFER FROM SPECIFIC CAPACITY DATA1

ABSTRACT: Specific capacity data obtained from Well Construction reports which are available from USGS offices, can provide useful estimates of tranamissivity (T), and hydraulic conductivity (K), of an aquifer. The Chicot Aquifer in Louisiana is one of the largest sources of fresh ground water in North America. Hydrologic data collected for the Chicot Aquifer indicate that specific capacity tests can be used in estimating local and regional values for T and K, if the Cooper-Jacob equation for transient flow is used with proper corrections for well loss and partial penetration. Where full scale pumping test data are scarce, specific capacity test data that are adequately distributed spatially can be used to map changes in T and K values and can be summarized statistically to indicate applicable regional values. A computer program called “TGUESS” which is available from International Ground Water Modeling Center, Holcomb Research Institute, was used in this study. The contour maps for T and K values are prepared for different well depth intervals to avoid wide variation of values.     

Testing of a GIS model ofEucalyptus Iargiflorens health on a semiarid,saline floodplain

Irrigated agriculture has resulted in substantial changes in water flows to the lower reaches of the River Murray. These changes have led to large-scale occurrences of dieback inEucalyptus largiflorens (black box) woodlands as well as increased inputs of salt to the river. Management options to address problems of this scale call for the use of spatial data sets via geographic information systems (GIS). A GIS exists for one floodplain of the River Murray at Chowilla, and a simple model predicted six health classes ofEucalyptus largiflorens based on groundwater salinity, flooding frequency, and groundwater depth.To determine the usefulness of the model for vegetation management, the quality of both the model and the GIS data sets were tested. Success of the testing procedure was judged by the degree of spatial matching between the model's predictions of health and that assessed from aerial photographs and by field truthing. Analyses at 80 sites showed that tree health was significantly greater where groundwater salinity was less than 40 dS/m or flooding occurred more frequently than 1 in 10 years or depth to groundwater exceeded 4 m. Testing of the GIS data sets found that vegetation was misclassified at 15% of sites. Association was shown between GIS-predicted values and field-truthed values of groundwater salinity but not groundwater depth. The GIS model of health is a useful starting point for future vegetation management and can be further improved by increasing the quality of the data coverages and further refining of the model to optimize parameters and thresholds.     

A Comparison of Sampling Designs for Monitoring Recreational Trail Impacts in Rocky Mountain National Park

The dual goals of the Organic Act of 1916 and Wilderness Act of 1964 are to protect natural resources and provide quality visitor experiences. Park managers need metrics of trail conditions to protect park resources and quality of visitor experiences. A few methods of sampling design for trails have been developed. Here, we describe a relatively new method, spatially balanced sampling, and compare it to systematic sampling. We evaluated the efficiency of sampling designs to measure recreation-related impacts in Rocky Mountain National Park. This study addressed two objectives: first, it compared estimates of trail conditions from data collected from systematic versus spatially balanced sampling data; second, it examined the relationship between sampling precision and sampling efficiency. No statistically significant differences in trail condition were found between the 100-m interval and the spatially balanced datasets. The spatially balanced probability-based dataset was found to be a good estimate of trail conditions when analyses were conducted with fewer sample points. Moreover, spatially balanced probability-based sampling is flexible and allows additional sample points to be added to a sample.     

Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping

The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of ≥6.25 m3/d/m (Case 1) and T values of ≥3.79 m2/d (Case 3) corresponding to a yield (Y) of ≥500 m3/d, and SPC values of ≥3.75 m3/d/m (Case 2) and T values of ≥2.61 m2/d (Case 4) corresponding to a Y of ≥300 m3/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W+ and W- of each factor's rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 71.20% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration.     

USING A GEOGRAPHIC INFORMATION SYSTEM TO DETERMINE THE RELATION BETWEEN STREAM QUALITY AND GEOLOGY IN THE ROBERTS CREEK WATERSHED,CLAYTON COUNTY,IOWA1

ABSTRACT: A geographic information system (GIS) was used to determine the relation between the stream-water quality and underlying geology in Roberts Creek watershed, Clayton County, Iowa, for base-flow conditions during the spring and summer of 1988–90. Geologic, stream, basin and subbasin boundaries, and water-quality sampling-site coverages were created by digitizing available maps. A contour coverage was created from digital line-graph data. The areal extent of geologic units subcropping in each subbasin was quantified with GIS, and the results then were output and joined with the discharge and water-quality data for statistical analyses. Illustrations showing the geology of the study area and the results of the study were prepared using GIS. By using GIS and a statistical software package, a weak but statistically significant relation was found between the water temperature, pH, and nitrogen concentrations in Roberts Creek and the underlying geology during base-flow conditions.     

INTEGRATION OF WATER QUALITY MODELING,REMOTE SENSING,AND GIS1

ABSTRACT: Water quality modeling has been developed for more than three quarters of a century, but is limited to the study of trends instead of making accurate short-term forecasts. A major barrier to water quality forecasting is the lack of an efficient system for water quality monitoring. Traditional water quality sampling is time-consuming, expensive, and can only be taken for small sizes. Remote sensing provides a new technique to monitor water quality repetitively for a large area. The objective of this research is to use remotely sensed data in a water quality model - QUAL2E - in a case study of the Te-Chi Reservoir in Taiwan. The water quality variables developed from the simulations are displayed in map form. The developed forecasting system is designed to predict water quality variables using remote sensing data as an input to initialize and update water quality conditions.     

Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas

Water quality regulation and litigation have elevated the awareness and need for quantifying water quality and source contributions in watersheds across the USA. In the present study, the regression method, which is typically applied to large (perennial) rivers, was evaluated in its ability to estimate constituent loads (NO(3)-N, total N, PO(4)-P, total P, sediment) on three small (ephemeral) watersheds with different land uses in Texas. Specifically, regression methodology was applied with daily flow data collected with bubbler stage recorders in hydraulic structures and with water quality data collected with four low-frequency sampling strategies: random, rise and fall, peak, and single stage. Estimated loads were compared with measured loads determined in 2001-2004 with an autosampler and high-frequency sampling strategies. Although annual rainfall and runoff volumes were relatively consistent within watersheds during the study period, measured annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Likewise, estimated loads were much better for the pasture watershed than the cultivated and mixed landuse watersheds because of more consistent land management and vegetation type in the pasture watershed, which produced stronger correlations between constituent loads and mean daily flow rates. Load estimates for PO(4)-P were better than for other constituents possibly because PO(4)-P concentrations were less variable within storm events. Correlations between constituent concentrations and mean daily flow rate were poor and not significant for all watersheds, which is different than typically observed in large rivers. The regression method was quite variable in its ability to accurately estimate annual nutrient loads from the study watersheds; however, constituent load estimates were much more accurate for the combined 3-yr period. Thus, it is suggested that for small watersheds, regression-based annual load estimates should be used with caution, whereas long-term estimates can be much more accurate when multiple years of concentration data are available. The predictive ability of the regression method was similar for all of the low-frequency sampling strategies studied; therefore, single-stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.     

Mapping soil textural fractions across a large watershed in north-east Florida

Assessment of regional scale soil spatial variation and mapping their distribution is constrained by sparse data which are collected using field surveys that are labor intensive and cost prohibitive. We explored geostatistical (ordinary kriging-OK), regression (Regression Tree-RT), and hybrid methods (RT plus residual Sequential Gaussian Simulation-SGS) to map soil textural fractions across the Santa Fe River Watershed (3585 km²) in north-east Florida. Soil samples collected from four depths (L1: 0–30 cm, L2: 30–60 cm, L3: 60–120 cm, and L4: 120–180 cm) at 141 locations were analyzed for soil textural fractions (sand, silt and clay contents), and combined with textural data (15 profiles) assembled under the Florida Soil Characterization program. Textural fractions in L1 and L2 were autocorrelated, and spatially mapped across the watershed. OK performance was poor, which may be attributed to the sparse sampling. RT model structure varied among textural fractions, and the model explained variations ranged from 25% for L1 silt to 61% for L2 clay content. Regression residuals were simulated using SGS, and the average of simulated residuals were used to approximate regression residual distribution map, which were added to regression trend maps. Independent validation of the prediction maps showed that regression models performed slightly better than OK, and regression combined with average of simulated regression residuals improved predictions beyond the regression model. Sand content >90% in both 0–30 and 30–60 cm covered 80.6% of the watershed area.     

Spatial Variability of Groundwater Depth and Quality Parameters in the National Capital Territory of Delhi

The groundwater quantity and quality scenario is of much concern in the National Capital Territory of Delhi, India, which necessitates an investigation to envisage the extent of spatial variability of groundwater depth and pollutant concentration levels in this region. Therefore, in this study, an effort was made to generate the spatial variability map of groundwater depth and quality parameters (viz. chloride, electrical conductivity, fluoride, magnesium, and nitrate). Ordinary kriging was used to analyze the spatial variability of groundwater depth and quality parameters, whereas indicator kriging was used to analyze groundwater quality parameters equal to or greater than the pollution threshold values. It was observed that the semivariogram parameters fitted well in the exponential model for water depth and in the spherical model for water quality parameters. The generated spatial variability maps indicated that in 43% of the study area, groundwater depth was within 20 m. The salinity level was higher than 2.5 dS m⁻¹ in 69% of the study area and the nitrate concentration exceeded 45 mg l⁻¹ in 36% of the area. The probability maps showed that about 24% of the area had the highest probability (0.8–1.0) of exceedence of the threshold electrical conductivity value and an area of 2% exhibited the highest probability of exceedence of the threshold value of nitrate concentration in the groundwater. The generated spatial variability and probability maps will assist water resource managers and policymakers in development of guidelines in judicious management of groundwater resources for agricultural and drinking purposes in the study area.     

Groundwater Vulnerability Assessment for Organic Compounds: Fuzzy Multicriteria Approach for Mexico City

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multicriteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km² or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.     

Filtering with a drill pump: an efficient method to collect suspended sediment

Water quality monitoring programs across multiple disciplines use total suspended solids (TSS), and volatile suspended solids (VSS), to assess potential impairments of surface water and groundwater. While previous methods for instream filtering have been developed, the need for rapid, cost‐effective, high volume sampling has increased with the need to verify and supplement data produced by sondes and instantaneous data loggers. We present an efficient method to filter water instream with a portable drill pump that results in reduced sample processing time, and potentially reduced error associated with sample transportation, preservation, contamination, and homogenization. This technical note outlines the advantages of filtering instream vs. in the laboratory. It also compares TSS and VSS concentrations filtered with a drill pump vs. standard filtration methods with a vacuum pump as outlined by USEPA methods 160.2 and 160.4. Samples were collected at 4 sites and filtered in the field, or transported to the laboratory and filtered within 12 or 24 h of collection. Overall TSS and VSS samples filtered instream with a drill pump vs. in the laboratory produced similar concentrations with a similar range in variability for each method. Sample filtering with a drill pump decreased processing time by five minutes per sample.     

Estimation of upper centile concentrations using historical atrazine monitoring data from community water systems

A survey sampling approach is presented for estimating upper centiles of aggregate distributions of surface water pesticide measurements obtained from datasets with large sample sizes but variable sampling frequency. It is applied to three atrazine monitoring programs of Community Water Systems (CWS) that used surface water as their drinking water source: the nationwide Safe Drinking Water Act (SDWA) data, the Syngenta Voluntary Monitoring Program (VMP), and the Atrazine Monitoring Program (AMP).The VMP/AMP CWS were selected on the basis of atrazine monitoring history (CWS having at least one annual average concentration from SDWA ≥ 1.6 ppb atrazine since 1997 in the AMP). Estimates of the raw water 95th, 99th, and 99.9th centile atrazine concentrations for the VMP/AMP CWS are 4.82, 11.85, and 34.00 ppb, respectively. The corresponding estimates are lower for the finished drinking water samples, with estimates of 2.75, 7.94, and 22.66 ppb, respectively. Finished water centile estimates for the VMP/AMP CWS using only the SDWA data for these sites are consistent with the results. Estimates are provided for the April through July period and for CWS based on surface water source type (static, flowing, or mixed). Requisite sample sizes are determined using statistical tolerance limits, relative SE, and the Woodruff interval sample size criterion. These analyses provide 99.9% confidence that the existing data include the 99.9th centile atrazine concentration for CWS raw and finished water in the Midwest atrazine high-use areas and in the nationwide SDWA dataset. The general validity of this approach is established by a simulation that shows estimates to be close to target quantities for weights based on sampling probabilities or time intervals between samples. Recommendations are given for suitable effective sample sizes to reliably determine interval estimates.     

Predicted uranium and radon concentrations in New Hampshire (USA) groundwater—Using Multi Order Hydrologic Position as predictors

Two radioactive elements, uranium (U) and radon (Rn), which are of potential concern in New Hampshire (NH) groundwater, are investigated. Exceedance probability maps are tools to highlight locations where the concentrations of undesirable substances in the groundwater may be elevated. Two forms of statistical analysis are used to create exceedance probability maps for U and Rn in NH groundwater. The first, Boosted Regression Tree (BRT), was selected for estimating U exceedance values. It computes exceedance values directly using the Bernoulli distribution function. The second method of statistical analysis used for Rn to determine exceedance probabilities is ordinary least squares (OLS) regression. In the process of determining exceedance probabilities for U and Rn, the utility of a new dataset is investigated. That new predictor dataset is the Multi-Order Hydrologic Position (MOHP) dataset. MOHP raster datasets have been produced nationally for the conterminous United States at a 30-m resolution. The concept behind MOHP is that, for any given point on the earth's surface, there is the potential for a longer groundwater flow path as one goes deeper beneath the land surface. MOHP predictors were tested in both models. Three MOHP predictors were found useful in the BRT model and two in the OLS model. MOHP data were found useful as predictors along with other site characteristics in predicting U and Rn exceedance probabilities in New Hampshire groundwater.     

Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.     

Use of ecosystem information to improve soil organic carbon mapping of a Mediterranean island

Detailed maps of soil C are needed to guide sustainable soil uses and management decisions. The quality of soil C maps of Italian Mediterranean areas may be improved and the sampling density reduced using secondary data related to the nature of the ecosystem. The current study was conducted to determine: (i) the improvements obtainable in mapping soil C over a Mediterranean island by using ecosystem features and (ii) the effect of different sampling densities on the map accuracy. This work relied on field sampling (n=164) of soil properties measured over the island of Pianosa (Central Italy). Statistical analysis assessing the relationship between soil properties and ecosystem features revealed that the conceptual model of ecosystems defined on the basis of environmental features such as vegetation cover, land use, and soil type was mainly related to the variation of soil organic carbon (OC) content and to the type of Mediterranean environment. The distribution of ecosystems was used to improve the accuracy of soil OC maps obtainable by a simple interpolation approach (ordinary kriging). Substantial improvement was obtained by: (i) stratification into ecosystem types and (ii) applying locally calibrated regressions to satellite imagery that introduced both inter-ecosystem and intra-ecosystem information linked to vegetation features. This study showed that interpolation methods using information on ecosystem distribution can produce accurate maps of soil OC in Mediterranean environments, mostly because of the linkage between soil OC and vegetation types, which are spatially fragmented and heterogeneous.     

Pre‐Development Groundwater Conditions Surrounding Memphis,Tennessee: Controversy and Unexpected Outcomes

Reliance on groundwater resources by differing governing bodies can create transboundary disputes raising questions of ownership and apportionment as the resource becomes strained through overuse or threatened by contamination. Transboundary disputes exist at varying scales, from conflicts between countries to smaller disputes between intrastate jurisdictions. In 2005 within the United States, the State of Mississippi filed a lawsuit against its political neighbor and their utility, the City of Memphis and Memphis Light, Gas, and Water, for groundwater deemed owned by the State of Mississippi to be wrongfully diverted across the state line and into Tennessee by the defendants. The basis of the lawsuit was potentiometric maps of groundwater levels for the Memphis aquifer that showed under suggested pre‐development conditions no flow occurring across the Mississippi‐Tennessee state line, but subsequent historic potentiometric maps show a cone of depression under the City of Memphis with a clear northwesterly gradient from Mississippi into Tennessee. The suggested pre‐development conditions were derived from limited groundwater level observations between 41 and 74 years post‐development. A new pre‐development map is constructed using historic records that range 0‐17 years post‐development that shows the natural flow is northwesterly from Mississippi into Tennessee and transboundary groundwater quantities have actually decreased since pre‐development conditions.