A conceptual model for response to Arctic oil spills

The potential for oil spills in Arctic regions has increased significantly because of the development of petroleum resources. Response to an oil spill in the Arctic is likely to be much slower than that in the temperate region because of the remoteness of the area and its severe climate. In the face of these unique problems, accurate prediction of the extent and subsequent movement of an oil spill is vital to any cleanup effort. Presented is the framework of a program to study the movement of oil spills in the Arctic. Existing models of oil spreading and polar ice dynamics are reviewed and areas where new model development is required are defined. A system design is developed that may be used for developing a plan to act in the event of a major spill.     

The Arctic Water Resource Vulnerability Index: An Integrated Assessment Tool for Community Resilience and Vulnerability with Respect to Freshwater

People in the Arctic face uncertainty in their daily lives as they contend with environmental changes at a range of scales from local to global. Freshwater is a critical resource to people, and although water resource indicators have been developed that operate from regional to global scales and for midlatitude to equatorial environments, no appropriate index exists for assessing the vulnerability of Arctic communities to changing water resources at the local scale. The Arctic Water Resource Vulnerability Index (AWRVI) is proposed as a tool that Arctic communities can use to assess their relative vulnerability-resilience to changes in their water resources from a variety of biophysical and socioeconomic processes. The AWRVI is based on a social-ecological systems perspective that includes physical and social indicators of change and is demonstrated in three case study communities/watersheds in Alaska. These results highlight the value of communities engaging in the process of using the AWRVI and the diagnostic capability of examining the suite of constituent physical and social scores rather than the total AWRVI score alone.     

Arctic Wilderness Policy in the United States and Finland

The United States and Finland have passed laws to classify and manage Arctic wilderness areas, but their national policies are based on different nature ideologies. Finns tend to perceive wilderness as a human-centered idea, while Americans are inclined to see the same land from a nature-based point of view. Rural residents in the Arctic, and especially indigenous peoples, use motorized vehicles for hunting and gathering in wilderness areas. Attempts of southern-based environmental groups to restrict motor use by imposing a nature-based ideology on rural residents in northern Alaska will result in high levels of political conflict. Alaska land managers need to respect the minority rights of rural residents and a study of wilderness policies in Finnish Lapland is instructive toward this end.     

Lake Temperature and Ice Cover Regimes in the Alaskan Subarctic and Arctic: Integrated Monitoring,Remote Sensing,and Modeling1

Arp, C.D., B.M. Jones, M. Whitman, A. Larsen, and F.E. Urban, 2010. Lake Temperature and Ice Cover Regimes in the Alaskan Subarctic and Arctic: Integrated Monitoring, Remote Sensing, and Modeling. Journal of the American Water Resources Association (JAWRA) 46(4): 777-791. DOI: 10.1111/j.1752-1688.2010.00451.x Abstract: Lake surface regimes are fundamental attributes of lake ecosystems and their interaction with the land and atmosphere. High latitudes may be particularly sensitive to climate change, however, adequate baselines for these lakes are often lacking. In this study, we couple monitoring, remote sensing, and modeling techniques to generate baseline datasets of lake surface temperature and ice cover in the Alaskan Subarctic and Arctic. No detectable trends were observed during this study period, but a number of interesting patterns were noted among lakes and between regions. The largest Arctic lake was relatively unresponsive to air temperature, while the largest Subarctic lake was very responsive likely because it is fed by glacial runoff. Mean late summer water temperatures were higher than air temperatures with differences ranging from 1.7 to 5.4°C in Subarctic lakes and from 2.4 to 3.2°C in Arctic lakes. The warmest mean summer water temperature in both regions was in 2004, with the exception of Subarctic glacially fed lake that was highest in 2005. Ice-out timing had high coherence within regions and years, typically occurring in late May in Subarctic and in early-July in Arctic lakes. Ice-on timing was more dependent on lake size and depth, often varying among lakes within a region. Such analyses provide an important baseline of lake surface regimes at a time when there is increasing interest in high-latitude water ecosystems and resources during an uncertain climate future.     

Tourism in the high north: Management challenges and recreation opportunity spectrum planning in Svalbard,Norway

The Arctic is subject to considerable pressure from a rapidly expanding world tourism industry and increasing demands for oil and gas resources. The archipelago of Svalbard in the Norwegian high arctic contains the most accessible islands in the entire Arctic, but despite extensive use of the resources, they still contain some of the last true wilderness lands in Europe. The growth in tourism with visitors from all parts of the world combined with coal mining and oil and gas exploration call for a coordinated environmental policy. A research-based management plan for tourism and recreation has now been developed. Some management problems were encountered in designing a recreation opportunity spectrum plan for this extremely vulnerable environment. Although Svalbard in many ways is a unique area, the challenges to management are comparable to those found in regions further south.     

STOCHASTIC ANALYSIS OF TIME-AGGREGATED HYDROLOGIC DATA1

Stochastic models fitted to hydrologic data of different time scales are interrelated because the higher time scale data (aggregated data) are derived from those of lower time scale. Relationships between the statistical properties and parameters of models of aggregated data and of original data are examined in this paper. It is also shown that the aggregated data can be more accurately predicted by using a valid model of the original data than by using a valid model of the aggregated data. This property is particularly important in forecasting annual values because only a few annual values are usually available and the resulting forecasts are relatively inaccurate if models based only on annual data are used. The relationships and forecasting equations are developed for general aggregation time and can be used for hourly and daily, daily and monthly or monthly and yearly data. The method is illustrated by using monthly and yearly streamflow data. The results indicate that various statistical characteristics and parameters of the model of annual data can be accurately estimated by using the monthly data and forecasts of annual data by using monthly models have smaller one step ahead mean square error than those obtained by using annual data models.     

TREND ASSESSMENT OF WATER QUALITY TIME SERIES1

ABSTRACT A general methodology is described for identifying and statistically modeling trends which may be contained in a water quality time series. A range of useful exploratory data analysis tools are suggested for discovering important patterns and statistical characteristics of the data such as trends caused by external interventions. To estimate the entries in an evenly spaced time series when data are available at irregular time intervals, a new procedure based upon seasonal adjustment is described. Intervention analysis is employed at the confirmatory data analysis stage to rigorously model changes in the mean levels of a series which are identified using exploratory data analysis techniques. Furthermore, intervention analysis can be utilized for estimating missing observations when they are not too numerous. The effects of cutting down a forest upon various water quality variables and also the consequences of acid rain upon the alkalinity in a stream provide illustrative applications which demonstrate the effectiveness of the methodology.     

PREDICTING BASE FLOW USING HYDROGEOLOGIC PARAMETERS1

ABSTRACT: A method is presented for predicting base flow using easily measured, or estimated, hydrogeologic parameters. A mathematical model based upon the theory of subsurface flow to parallel drains is applied to a small watershed in Oklahoma. An example of model application is presented for a five-year period of record from this small watershed. Three years of data are used to calibrate the model, and two years of data are used for model validation. Hydrographs of observed and predicted base flow are presented for the five-year period of record. We concluded from this limited application of the model, on a small watershed, that the modeling techniques discussed herein were valid and should be tested for longer time periods on a larger watershed to determine their general applicability.     

APPROACHES TO MULTIVARIATE MODELING OF WATER RESOURCES TIME SERIES1

ABSTRACT: Alternative approaches suggested for modeling multiseries of water resources systems are reviewed and compared. Most approaches fall within the general framework of multivariate ARMA models. Formal modeling procedures suggest a three-stage iterative process, namely: model identification, parameter estimation and diagnostic checks. Although a number of statistical tools are already available to follow such modeling process, in general, it is not an easy task, especially if high order vector ARMA models are used. However, simpler ARMA models such as the contemporaneous and the transfer-function models may be sufficient for most applications in water resources. Two examples of modeling bivariate and trivariate streamflow series are included. Alternative modeling procedures are used and compared by using data generation techniques. The results obtained suggest that low order models, as well as contemporaneous ARMA models, reproduce quite well the main statistical characteristics of the time series analyzed. It is assumed that the same conclusions apply for most water resources time series.     

THE RELATIONSHIP BETWEEN THE TIME BASES OF SIMULATION MODELS AND THEIR STRUCTURE1

ABSTRACT: The time base of a simulation model can be defined as a combination of two time intervals. One is the interval used for input and internal computations. The second is the interval used for the output and calibration of the model. The time base of a model is related on the one hand to the type of applications for which the simulated data are used, and on the other hand to the structure and complexity of the model. The latter may be represented by the number of parameters employed to specify the operation of the model. Using data typical to relatively small watersheds in a semiarid climate, the interaction between the complexity of a series of models and the time bases used by them was studied. This included the effects of the two factors, time base and complexity, on the values of the optimal parameters, prediction of mean annual flow, and general performance of the models. The main conclusion is that if the acceptable time base is longer, the model can be less complex needing fewer parameters. There is also an advantage in using a time base comprising a shorter input time interval and a longer output time interval.     

An innovative modeling approach using Qual2K and HEC-RAS integration to assess the impact of tidal effect on River Water quality simulation

Water quality modeling has been shown to be a useful tool in strategic water quality management. The present study combines the Qual2K model with the HEC-RAS model to assess the water quality of a tidal river in northern Taiwan. The contaminant loadings of biochemical oxygen demand (BOD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and sediment oxygen demand (SOD) are utilized in the Qual2K simulation. The HEC-RAS model is used to: (i) estimate the hydraulic constants for atmospheric re-aeration constant calculation; and (ii) calculate the water level profile variation to account for concentration changes as a result of tidal effect. The results show that HEC-RAS-assisted Qual2K simulations taking tidal effect into consideration produce water quality indices that, in general, agree with the monitoring data of the river. Comparisons of simulations with different combinations of contaminant loadings demonstrate that BOD is the most import contaminant. Streeter-Phelps simulation (in combination with HEC-RAS) is also performed for comparison, and the results show excellent agreement with the observed data. This paper is the first report of the innovative use of a combination of the HEC-RAS model and the Qual2K model (or Streeter-Phelps equation) to simulate water quality in a tidal river. The combination is shown to provide an alternative for water quality simulation of a tidal river when available dynamic-monitoring data are insufficient to assess the tidal effect of the river.     

A WATERSHED APPROACH TO ECOSYSTEM MONITORING IN DENALI NATIONAL PARK AND PRESERVE,ALASKA1

ABSTRACT: The National Park Service and the National Biological Service initiated research in Denali National Park and Preserve, a 2.4 million-hectare park in southcentral Alaska, to develop ecological monitoring protocols for national parks in the Arctic/Subarctic biogeographic area. We are focusing pilot studies on design questions, on scaling issues and regionalization, ecosystem structure and function, indicator selection and evaluation, and monitoring technologies. Rock Creek, a headwater stream near Denali headquarters, is the ecological scale for initial testing of a watershed ecosystem approach. Our conceptual model embraces principles of the hydrological cycle, hypotheses of global climate change, and biological interactions of organisms occupying intermediate, but poorly studied, positions in Alaskan food webs. The field approach includes hydrological and depositional considerations and a suite of integrated measures linking key aquatic and terrestrial biota, environmental variables, or defined ecological processes, in order to establish ecological conditions and detect, track, and understand mechanisms of environmental change. Our sampling activities include corresponding measures of physical, chemical, and biological attributes in four Rock Creek habitats believed characteristic of the greater system diversity of Denali. This paper gives examples of data sets, program integration and scaling, and research needs.     

Limitations of model-fitting methods for kinetic analysis: Polystyrene thermal degradation

In this paper, some clarifications regarding the use of model-fitting methods of kinetic analysis are provided in response to the lack of plot linearity and dispersion in the activation energy values for the thermal degradation of polystyrene found in the literature and some results proposing an nth order model as the most suitable one. In the present work, two model-fitting methods based on the differential and integral forms of the general kinetic equation are evaluated using both simulated and experimental data, showing that the differential method is recommended due to its higher discrimination power. Moreover, the intrinsic limitations of model-fitting methods are highlighted: the use of a limited set of kinetic models to fit experimental data and the ideal nature of such models. Finally, it is concluded that a chain scission model is more appropriate than first order.     

A Hydrological Model for Predicting the Effects of Dams on the Shoreline Vegetation of Lakes and Reservoirs

/ The species richness of shoreline vegetation of unregulated lakes in Nova Scotia, Canada, is known to increase as a function of catchment area, a topographic variable governing water level fluctuations. Predictions based on catchment area however, fail to account for richness patterns at the margins of lakes enlarged by dams. Here, we compare the vegetation and hydrological regimes of regulated and unregulated systems. Hydrological regimes of regulated systems deviated from natural systems of similar catchment area by being either hypovariable or hypervariable for both within-year and among-year fluctuations in water level. Plant communities of dammed systems were less diverse, contained more exotic species, and were, with one exception, devoid of rare shoreline herbs. Data from "recovering," or previously dammed systems indicated that shoreline communities can be restored upon return of the appropriate hydrological regime. Using observed within-year and among-year water level fluctuation data, we propose a general model for the maintenance or restoration of diverse herbaceous wetlands on shorelines of temperate lakes or reservoirs. Managers can manipulate the within-year water level variation within prescribed limits (1-2 m), while ensuring that among-year variation (SD of summer levels) is less than 25% of within-year variation. This preliminary model is based on data from low-fertility, temperate lakes in river systems. To calibrate the model, plant community data from other regions are needed, as are long-term water-level data for unregulated lakes, data which are essential but largely lacking in many areas.KEY WORDS: Catchment area; Regulated lakes; Shoreline restoration; Rare plants; Exotic plants; Diversity     

WATER DIVERSIONS TO THE NORTH AMERICAN PRAIRIES1

ABSTRACT .The major purpose of existing and proposed diversions of water to the Prairies is for irrigation. Being flat lands adaptable to mechanized agriculture and possessing a good network of rail and highway access, they are well suited for such use. Wheat surpluses, currently a serious problem in Canada, can be expected to melt under world population pressures and conversion of wheat lands to other crops with the advent of irrigation. The water sources envisaged are in the Canadian Arctic watershed with the United States purchasing Canadian water at the boundary. There is an increasing interest in the Canadian Prairie Provinces in regarding water as a commodity and selling some of it to pay for the development required to put the portion retained in Canada to use.     

SIMULATING SOIL WATER RECESSION COEFFICIENTS FOR AGRICULTURAL WATERSHEDS1

ABSTRACT: The antecedent precipitation index (API) has been a useful indicator of soil moisture conditions for watershed runoff calculations, and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. The prognostic equation for soil moisture used in some of the atmospheric general circulation models (GCM) together with Thomthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors as contained in potential evapotranspiration and to depend on soil texture as reflected by field capacity and permanent wilting point. A recently developed model for global insolation is used with climatological data for Wisconsin to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trends at Fennimore and Colby watersheds in Wisconsin. This study suggests that API could be a unifying concept for watershed and atmospheric general circulation modeling.     

A DYNAMIC SYSTEM MODEL FOR SIMULATING SEDIMENT DISCHARGE1

ABSTRACT: A dynamic sediment discharge model was developed and proposed for the simulation of watershed systems. It war developed from an expansion of splash and flow erosion relationships under steady state conditions. It was described as a general erosion model that can be reduced to forms comparable to many conceptual soil erosion and sediment yield models. The model incorporates eight parameters such as rainfall intensities, runoff rates, and previous sediment discharges. The model was tested with two small watersheds with simulation results which were very satisfactory compared to the data.     

EVALUATION OF PROBABILITY DENSITY FUNCTIONS IN PRECIPITATION MODELS FOR THE PACIFIC NORTHWEST1

ABSTRACT: Recent work has found that a one-parameter Weibull model of wet day precipitation amount based on the Weibull distribution provides a better fit to historical daily precipitation data for eastern U.S. sites than other one-parameter models. The general two-parameter Weibull distribution was compared in this study to other widely used distributions for describing the distribution of daily precipitation event sizes at 99 sites from the U.S. Pacific Northwest. Surprisingly little performance was sacrificed by reducing the two-parameter Weibull to a single-parameter distribution. Advantages of the single-parameter model included requiring only the mean wet day precipitation amount for calibration, invertibility for simulation purposes, and ease of analytical manipulation. The fit of the single-parameter Weibull to the 99 stations included in this study was significantly better than other single-parameter models tested, and performed as well as the widely endorsed, more cumbersome, two-parameter gamma model. Both the one-and two-parameter Weibull distributions are shown to have b-moments that are consistent with historical precipitation data, while the ratio of b-skew and b-variance in the gamma model is inconsistent with the historical recerd by this measure. In addition, it was found that the two-parameter gamma distribution was better fit using the method of moments estimators than maximum likelihood estimates. These findings suggested that the distribution in precipitation among sites in the Pacific Northwest with dramatically different settings are nearly identical if expressed in proportion to the mean site event size.     

Assessing Public Perceptions of Computer-Based Models

Although there is a solid body of research on both collaborative decision-making and on processes using models, there is little research on general public attitudes about models and their use in making policy decisions. This project assessed opinions about computer models in general and attitudes about a specific model being used in water planning in the Middle Rio Grande Region of New Mexico, United States. More than 1000 individuals were surveyed about their perceptions of computer-based models in general. Additionally, more than 150 attendees at public meetings related to the Middle Rio Grande planning effort were surveyed about their perceptions of the specific Rio Grande-based model. The results reveal that the majority of respondents are confident in their ability to understand models and most believe that models are appropriate tools for education and for making policy decisions. Responses also reveal that trust in who develops a model is a key issue related to public support. Regarding the specific model highlighted in this project, the public revealed tremendous support for its usefulness as a public engagement tool as well as a tool to assist decision-makers in regional water planning. Although indicating broad support for models, the results do raise questions about the role of trust in using models in contentious decisions.     

MULTIPLICATIVE,SEASONAL ARIMA MODELS FOR LAKE ERIE AND LAKE ONTARIO WATER LEVELS1

ABSTRACT: Federal agencies in the U.S. and Canada continuously examine methods to improve understanding and forecasting of Great Lakes water level dynamics in an effort to reduce the negative impacts of fluctuating levels incurred by interests using the lakes. The short term, seasonal and long term water level dynamics of lakes Erie and Ontario are discussed. Multiplicative, seasonal ARIMA models are developed for lakes Erie and Ontario using standardized, monthly mean level data for the period 1900 to 1986. The most appropriate model identified for each lake had the general form: (1 0 1)(0 1 1)₁₂. The data for each lake were subdivided by time periods (1900 to 1942;1 943 to 1986) and the model coefficients estimated for the subdivided data were similar, indicating general model stability for the entire period of record. The models estimated for the full data sets were used to forecast levels 1,2,3, and 6 months ahead for a period of high levels (1984 to 1986). The average absolute forecast error for Lake Erie was 0.049m, 0.076m, 0.091 m and 0.128m for the 1, 2,3, and 6 month forecasts, respectively. The average absolute forecast error for Lake Ontario was 0.058m, 0.095m, 0.120m and 0.136m for the 1,2,3, and 6 month forecasts, respectively. The ARIMA models provide additional information on water level time series structure and dynamics. The models also could be coordinated with current forecasting methods, possibly improving forecasting accuracy.