AN ANALYSIS OF SEASONAL FECAL COLIFORM LEVELS IN THE TCHEFUNCTE RWER1

ABSTRACT: A model for estimating seasonal fecal coliform concentrations in the Tchefuncte River as a function of river discharge was developed. Data on fecal coliform concentration were obtained from the Louisiana Department of Health and Hospitals and were available for a period of 15 years (1975 through 1992) from three locations. Stream flow data were obtained from a gaging station of the U. S. Geological Survey at Folsom, Louisiana. These data were available for 49 years (1943 through 1991). The climate of the area is characterized by different precipitation/runoff mechanisms for the summer and winter seasons. The division for seasons used in this analysis was May through October (summer season), and November through April (winter season). Because of the combined effects of climatic mechanisms causing precipitation and the seasonal variation of evapotranspiration, runoff is greater in the winter season resulting in higher fecal coliform counts in the Tchefuncte River. Statistical analysis revealed a statistically significant relationship between fecal coliform concentration and discharge for each season, at each of three sites on the Tchefuncte River.     

Global Warming and Global Dioxide Emission: An Empirical Study

In this paper, the dynamic relationship between global surface temperature (global warming) and global carbon dioxide emission (CO₂) is modelled and analyzed by causality and spectral analysis in the time domain and frequency domain, respectively. Historical data of global CO₂emission and global surface temperature anomalies over 129 years from 1860–1988 are used in this study. The causal relationship between the two phenomena is first examined using the Sim and Granger causality test in the time domain after the data series are filtered by ARIMA models. The Granger causal relationship is further scrutinized and confirmed by cross-spectral and multichannel spectral analysis in the frequency domain. The evidence found from both analyses proves that there is a positive causal relationship between the two variables. The time domain analysis suggests that Granger causality exists between global surface temperature and global CO₂emission. Further, CO₂emission causes the change in temperature. The conclusions are further confirmed by the frequency domain analysis, which indicates that the increase in CO₂emission causes climate warming because a high coherence exists between the two variables. Furthermore, it is proved that climate changes happen after an increase in CO₂emission, which confirms that the increase in CO₂emission does cause global warming.     

Relative Influence of Streamflows in Assessing Temporal Variability in Stream Habitat1

Abstract: The effects of streamflows on temporal variation in stream habitat were analyzed from the data collected 6‐11 years apart at 38 sites across the United States. Multiple linear regression was used to assess the variation in habitat caused by streamflow at the time of sampling and high flows between sampling. In addition to flow variables, the model also contained geomorphic and land use factors. The regression model was statistically significant (p < 0.05; R² = 0.31‐0.46) for 5 of 14 habitat variables: mean wetted stream depth, mean bankfull depth, mean wetted stream width, coefficient of variation of wetted stream width, and the percent frequency of bank erosion. High flows between samples accounted for about 16% of the total variation in the frequency of bank erosion. Streamflow at the time of sampling was the main source of variation in mean stream depth and contributed to the variation in mean stream width and the frequency of bank erosion. Urban land use (population change) accounted for over 20% of the total variation in mean bankfull depth, 15% of the total variation in the coefficient of variation of stream width, and about 10% of the variation in mean stream width.     

PREDICTING FECAL COLIFORM BACTERIA LEVELS IN THE CHARLES RIVER,MASSACHUSETTS, USA1

In Massachusetts, the Charles River Watershed Association conducts a regular water quality monitoring and public notification program in the Charles River Basin during the recreational season to inform users of the river's health. This program has relied on laboratory analyses of river samples for fecal coliform bacteria levels, however, results are not available until at least 24 hours after sampling. To avoid the need for laboratory analyses, ordinary least squares (OLS) and logistic regression models were developed to predict fecal coliform bacteria concentrations and the probabilities of exceeding the Massachusetts secondary contact recreation standard for bacteria based on meteorological conditions and streamflow. The OLS models resulted in adjusted R²s ranging from 50 to 60 percent. An uncertainty analysis reveals that of the total variability of fecal coliform bacteria concentrations, 45 percent is explained by the OLS regression model, 15 percent is explained by both measurement and space sampling error, and 40 percent is explained by time sampling error. Higher accuracy in future bacteria forecasting models would likely result from reductions in laboratory measurement errors and improved sampling designs.     

TIME SERIES ANALYSIS IN PERSPECTIVE1

ABSTRACT: By employing a set of criteria for classifying the capabilities of time series models, recent developments in time series analysis are assessed and put into proper perspective. In particular, the inherent attributes of a wide variety of time series models and modeling procedures presented by the authors of the 18 papers contained in this volume are clearly pointed out. Additionally, it is explained how these models can address many of the time series problems encountered when modeling hydrologic, water quality and other kinds of time series. For instance, families of time series models are now available for modeling series which may contain nonlinearities or may follow nonGaussian distributions. Based upon a sound physical understanding of a problem and results from exploratory data analyses, the most appropriate model to fit to a data set can be found during confirmatory data analyses by following the identification, estimation and diagnostic check stages of model construction. Promising future research projects for developing flexible classes of time series models for use in water resources applications are suggested.     

TEMPORAL VARIABILITY OF WATER QUALITY IN THE ST. LUCIE ESTUARY,SOUTH FLORIDA1

ABSTRACT: Four years of monthly freshwater discharge and constituent concentration data from three tributaries were related to a concurrent series of data for three segments of the St. Lucie Estuary in South Florida using multiple regression and time-series analysis techniques. Water quality parameters examined were dissolved inorganic and total nitrogen and phosphorus, chlorophyll a, total suspended solids, turbidity, and color. On monthly time scales, a multiple regression, which included freshwater discharge, freshwater constituent concentration, and dilution with ocean water (salinity) as independent variables, explained 50 percent or less of the variability in estuarine constituents. No single independent variable explained more variation than another. By contrast, on seasonal (wet, dry) time scales, freshwater discharge explained the bulk of variation in estuarine water quality (up to 93 percent). On monthly time scales, variability in concentrations of nutrients and other constituents may be largely controlled by processes internal to the system. At seasonal time scales, freshwater discharge appears to drive variability in most estuarine water quality parameters examined. Results indicate that management of tributary input on a seasonal basis, with the expectation of achieving seasonal concentration goals in the estuary, would have a higher probability of success than managing on a monthly basis.     

Global monitoring of wetlands--the value of ENVISAT ASAR Global mode

This paper elaborates on recent advances in the use of ScanSAR technologies for wetland-related research. Applications of active satellite radar systems include the monitoring of inundation dynamics as well as time series analyses of surface soil wetness. For management purposes many wetlands, especially those in dry regions, need to be monitored for short and long-term changes. Another application of these technologies is monitoring the impact of climate change in permafrost transition zones where peatlands form one of the major land cover types. Therefore, examples from boreal and subtropical environments are presented using the analysed ENVISAT ASAR Global mode (GM, 1 km resolution) data acquired in 2005 and 2006. In the case of the ENVISAT ASAR instrument, data availability of the rather coarse Global Mode depends on request priorities of other competing modes, but acquisition frequency may still be on average fortnightly to monthly depending on latitude. Peatland types covering varying permafrost regimes of the West Siberian Lowlands can be distinguished from each other and other land cover by multi-temporal analyses. Up to 75% of oligotrophic bogs can be identified in the seasonal permafrost zone in both years. The high seasonal and inter-annual dynamics of the subtropic Okavango Delta can also be captured by GM time series. Response to increased precipitation in 2006 differs from flood propagation patterns. In addition, relative soil moisture maps may provide a valuable data source in order to account for external hydrological factors of such complex wetland ecosystems.     

Scale-dependent inconsistencies in the effects of trampling on a forest understory community

The response of forest understory vegetation to trampling applied at different temporal and spatial scales was determined in a cliff-edge forest in Ontario, Canada. Three frequencies (0, 50, 500 passes per year) of short-term trampling (one year) were applied to plots previously undisturbed. Existing trails that had received three frequencies (approx. 100, 500, 25,000 passes per year) of long-term trampling (18 years) were also studied. Community composition, species richness, and individual species frequency were recorded in plots within 4 m and (or) 1 m of the patch centerline. The quantitative and qualitative form of plant response to increased trampling was compared for short-term and long-term treatments, both within 4 m and within 1 m of the path centerline, to judge the consistency of trampling effects at different temporal and spatial scales. As trampling frequency increased, community composition changed progressively, but consistently, in plots both within 4 m and 1 m of the path centerline. Species richness was less affected by trampling and only decreased within 1 m of the path centerline at the highest level of trampling (25,000 passes per season for 18 years). Effects of trampling on individual species frequency were much less consistent at different temporal and spatial scales of trampling. The scale-dependence results suggest that field workers and resource managers both should try explicitly to include and define multiple scale components when trying to ascertain the response of vegetation to human disturbance factors.     

FOREST HYDROLOGY AND EXTREME RAINFALL FREQUENCY1

Extreme rainfall frequency analysis provides one means to predict, within certain limits of probability, the average time interval between the recurrences of storms of a specified duration and magnitude. This in turn furnishes the forest hydrologist a valuable tool for engineering design and runoff and erosion forecast. A modification in the application of the annual maximum and annual exceedance series analysis described by V. T. Chow can, for special purposes, lead to an even more useful estimate of extreme events on a seasonal basis. This can be particularly important on small forested headwater watersheds where the runoff response to extreme rainfall may vary considerably with seasonal changes in canopy cover and soil moisture characteristics. Although the application of data covering a relatively short period of record has produced some inconsistencies among the frequency diagrams, under certain circumstances for short-term recurrence interval forecast and for non-critical application the analysis of extreme rainfall frequency from less than 20 years data seems justified.     

VISUALIZATION OF TRENDS AND FLUCTUATIONS IN CLIMATIC RECORDS1

ABSTRACT: Small systematic changes in climatic records are often poorly visualized by standard time series plots because they are usually hidden by the magnitude and variability of the data values themselves. A visualization approach based on the rescaled adjusted partial sums (RAPS) which overcomes the above-stated shortcomings is presented. This visualization highlights trends, shifts, data clustering, irregular fluctuations, and periodicities in the record. Additional information on the number, magnitude, shape, frequency, and timing of fluctuations and trends can also be inferred. The visualization approach can be used for preliminary visual inspection of a time series, to gain a feel for the data, and/or to guide and focus subsequent statistical tests and analyses. It is not intended as a substitute for standard statistical analysis. Alternatively, the visualization approach can be used to display findings of a time series analysis. The capabilities and limitations of the approach are discussed and illustrated for two time series of annual rainfall values.     

OREGON WATER QUALITY INDEX A TOOL FOR EVALUATING WATER QUALITY MANAGEMENT EFFECTIVENESS1

ABSTRACT: The Oregon Water Quality Index (OWQI) is a single number that expresses water quality by integrating measurements of eight water quality variables (temperature, dissolved oxygen, biochemical oxygen demand, pH, ammonia+nitrate nitrogen, total phosphorus, total solids, and fecal coliform). Its purpose is to provide a simple and concise method for expressing the ambient water quality of Oregon's streams for general recreational use, including fishing and swimming. The OWQI, originally developed in the 1970s, has been updated based upon improved understanding about water quality behavior. This report describes the historical basis of the OWQI and defines the improved design of the present OWQI. The index allows users to easily interpret data and relate overall water quality variation to variations in specific categories of impairment. This report demonstrates the value of the OWQI in presenting spatial and temporal water quality information. The OWQI improves comprehension of general water quality issues, communicates water quality status, and illustrates the need for and effectiveness of protective practices.     

MODELING FECAL COLIFORM CONTAMINATION IN THE RIO GRANDE1

This study examines sources of fecal coliform in Segment 2302 of the Rio Grande, located south of the International Falcon Reservoir in southern Texas. The watershed is unique because the contributing drainage areas lie in Texas and Mexico. Additionally, the watershed is mostly rural, with populated communities known as “colonias.” The colonias lack sewered systems and discharge sanitary water directly to the ground surface, thus posing an increased health hazard from coliform bacteria. Monitoring data confirm that Segment 2302 is not safe for contact recreation due to elevated fecal coliform levels. The goal of the study was to simulate the observed exceedences in Segment 2302 and evaluate potential strategies for their elimination. Fecal coliform contributions from ranching and colonia discharges were modeled using the Hydrologic Simulation Program‐Fortran (HSPF). Model results indicated that the regulatory 30‐day geometric mean fecal coliform concentration of 200 colony forming units (cfu) per 100 milliliters is exceeded approximately three times per year for a total of 30 days. Ongoing initiatives to improve wastewater facilities will reduce this to approximately once per year for 14 days. Best management practices will be necessary to reduce cattle access to streams and eliminate all exceedences. The developed model was limited by the relatively sparse flow and fecal coliform data.     

0.1～3000MHz射频电磁环境时间变化规律及其与人群作息时间关系的研究

利用自动连续监测仪器对在成都市城区选定的监测点位开展了持续数日的测量,获取了测点的长时间监测数据,通过对监测数据的分析,研究了0.1~3000MHz射频电磁环境随时间变化的规律及其与人群作息时间的关系,并对电磁环境人工手动监测的适宜时间段进行了分析。     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY1

ABSTRACT: Fractional differencing is a tool for modeling time series which have long-term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box-Jenkins ARIMA(p, d, q) models to take nonintegral values; they permit the simultaneous modeling of the long-term and short-term behavior of an observed time series. This paper discusses the usefulness of fractional differencing to time-series modeling, with emphasis on hydrologic applications. A methodology for fitting fractionally differenced ARIMA models is described, and examples are presented.     

Water quality trends at inflows to Everglades National Park, 1977-2005

Restoration of the Florida Everglades is important for the health of the natural system, including both the "River of Grass" and its downstream estuaries. Water quality improvement is one indicator of successful restoration in this complex ecosystem. Using the period of record of 1977 through 2005, we evaluated data from seven inflow sites to the Everglades National Park (ENP) for temporal trends of various forms of phosphorus (P) and nitrogen (N) and analyzed them using principal component analysis and factor analysis without flow adjustments. Locally estimated scatter plot smoothing (LOESS) trend lines identified two inflection points (three time periods) of changing trend in total P (TP) concentration at the seven sites. Results indicated that overall water quality in ENP inflow improved from 1977 to 2005, with significant downward trends in TP concentration. The overall trend ofTP is probably mediated by hydrology, which is evident by a negative relationship between flow and annual average TP concentration at the majority of stations within the available data, although additional changes in vegetation due to hydroperiod may have some effects. Total N (TN), total Kjeldahl N, and total organic N concentrations also generally decreased at inflow sites. Water quality standards for TP, TN, and NH4+ -N were exceeded at selected sites during the study period. Principle component analysis and factor analysis detected a grouping of sampling sites related to the water delivery system that could be used as indicators to better manage monitoring resources. Study results suggest that water quality data analyses could provide additional insight into the success of a restoration management plan and on how monitoring may be modified for more efficient use ofresources.     

Latent effect of soil organic matter oxidation on mercury cycling within a southern boreal ecosystem

The focus of this study is to investigate processes causing the observed spatial variation of total mercury (THg) in the soil O horizon of watersheds within the Superior National Forest (Minnesota) and to determine if results have implications toward understanding long-term changes in THg concentrations for resident fish. Principal component analysis was used to evaluate the spatial relationships of 42 chemical elements in three soil horizons over 10 watersheds. Results indicate that soil organic carbon is the primary factor controlling the spatial variation of certain metals (Hg, Tl, Pb, Bi, Cd, Sn, Sb, Cu, and As) in the O and A soil horizons. In the B/E horizon, organic carbon appeared to play a minor role in metal spatial variation. These characteristics are consistent with the concentration of soil organic matter and carbon decreasing from the O to the B/E horizons. We also investigated the relationship between percent change in upland soil organic content and fish THg concentrations across all watersheds. Statistical regression analysis indicates that a 50% reduction in age-one and age-two fish THg concentration could result from an average 10% decrease in upland soil organic content. Disturbances that decrease the content of THg and organic matter in the O and A horizons (e.g., fire) may cause a short-term increase in atmospherically deposited mercury but, over the long term, may lead to decreased fish THg concentrations in affected watersheds.     

SPATIAL AND TEMPORAL WATER QUALITY VARIABILITY IN THE PIRACICABA RIVER BASIN,BRAZIL1

ABSTRACT: The spatial and temporal variability of dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate concentration and total coliform (TC) were investigated at nine sampling stations distributed along the main rivers of the Piracicaba River Basin, a 12,400 km² catchment located in São Paulo State, one of the most developed regions of Brazil. Spatially, a downstream impoverishment of water quality conditions was observed, as seen by the decrease of DO, and increase of BOD, nitrate, and TC. These changes were probably caused by accumulating downstream discharge of domestic and industrial sewage. Temporal evaluation of 18 years of data showed that DO decreased with time for the majority of the sampling stations, while BOD, nitrate, and TC increased. A law, approved at the end of 1991, proposed a new water tax for river water extraction for industrial and agricultural use. The amount of this tax is determined according to the water quality of the extracted water. Therefore, the evaluation of the water quality status in this basin is a first step to help resources managers to determine the values for this tax.     

PAST AND FUTURE OF ANALYSIS OF WATER RESOURCES TIME SERIES1

ABSTRACT: water resources supply and demand time series consist of several or all of the four basic characteristics: tendency, intermittency, periodicity and stochasticity. Their importance changes from one type of variables to another. Historic developments of analysis of time series in hydrology have varied significantly over the past, from the stress on search for periodicities and persistence in annual series to the emphasis on the series stochastic properties. Supply and demand series are often highly interrelated, which fact is most often neglected in planning water resources systems in general, and water storage capacities in particular. The future of series analysis in water resources will likely be by a joint use of physically-based structural analysis and the use of advanced methods of treating data by stochastic processes, statistical estimation and inference techniques. The most intriguing challenge of the future of this analysis may be the treatment of nonnormal, nonlinear and in general nonstationary hydrologic and water use time series. The proper treatment of complex multivariate processes will also challenge the specialists, especially for the purposes of transfer of information between data on variables at given points, or between data at several points of a given variable, or both.