Climate Policy in Light of Climate Science: The ICLIPS Project

The paper introduces the Tolerable Windows Approach (TWA) asa decision analytical framework for addressing global climatechange. It is implemented as an integrated assessment model (IAM)developed in the project on Integrated Assessment of ClimateProtection Strategies (ICLIPS). The background and the main objectivesof the project are described and its relationships to other currentintegrated assessment efforts are elucidated. Key features ofthe TWA are compared with those of cost-benefit and cost-effectivenessframeworks. An overview of the ICLIPS IAM framework is providedtogether with its methodological foundations. Main features ofthe individual models are presented, covering the climate, theaggregated economic, and the impact models. Additional componentsof the framework include dynamic mitigation cost functions andan agriculture/land-use model (both incorporated into the fullyintegrated ICLIPS climate-economy model) as well as a globalmulti-region, multi-sector, dynamic general equilibrium model.     

Integrated Assessment of Long-term Climate Policies: Part 2 – Model Results and Uncertainty Analysis

An integrated assessment model (IAM) is applied to explore optionsfor long-term climate policy by identifying permitted emissioncorridors. The options are determined under various assumptionsabout constraints related to acceptable impacts of climate changein terms of alterations induced in natural ecosystems in protectedareas and about acceptable mitigation costs, burden sharingprinciples, and implementation flexibility. The results show thatabout 25% of the protected areas worldwide will witness ecosystemtransformation in the next century even if the costs of emissionreduction are allowed to reach 2% of per-capita consumption. Anuncertainty analysis surveys the implications of modifyingselected key model variables on the existence and shape of theemission corridors. Within plausible ranges of parametervariations, the emission corridor turns out to be rather sensitiveto impact constraints, climatic constraints like the magnitude andrate of the global mean temperature increase, and to aerosolemission scenarios.     

Application of Artificial Neural Networks to Complex Groundwater Management Problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.     

Hydraulic parameters of a multilayered aquifer system in Kuwait City

The Kuwait Group consists mainly of clastic sediments overlying unconformably the Dammam Formation of Tertiary age. The Kuwait Group is generally divided into three main hydrostratigraphic units: the upper and lower aquifers separated by an aquitard. The upper aquifer is further divided into the water table aquifer, an aquitard and a semiconfined aquifer. This semiconfined unit was pumped and the drawdowns were observed in piezometers screened in various subunits of the Kuwait Group. Some pumping tests of short duration were carried out in the top water table aquifer as well. These tests showed that the subunits of the Kuwait Group are hydraulically interconnected to a varying degree.

The pumping test data were analysed using conventional analytical solutions. The semiconfined pumping test was also simulated by a quasi-three-dimensional model using a leaky multiaquifer modelling technique. The initial hydraulic parameters were improved manually in the model till best fit drawdowns were obtained.

The final parameters obtained by simulation of the pumping tests were used in designing a pilot drainage system for the control of a rising groundwater table in parts of Kuwait City.     

Human impact on fixed sand dunes revealed by morphometric analysis

The Nyírség is the second largest alluvial fan in Hungary covered by fixed sand dunes. The primary aim of the paper is to describe the morphology of dunes in the region and classify them based on their morphometric characteristics. The other major aim is to select those dunes which were exposed to significant anthropogenic impact, and to determine the spatial and temporal differences in the intensity of human activity. The following dune types were separated: valley‐marginal, transitional valley‐marginal, transitional parabolic, filled, partially and unfilled parabolic dunes. After defining different dune types and their parameters, certain dunes were selected based on exposure to significant anthropogenic impact. Definite connection was demonstrated between the intensity of human environmental impact and the rate of erosion on fixed sand dunes. The erosion of sand dunes was most intensive in Medieval times, most likely due to concentration of agricultural land use. Copyright © 2009 John Wiley & Sons, Ltd.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.     

Stochastic forecasting of mine water inrushes

An event-based stochastic forecasting approach is used to model water inrushes into underground works under karstic water hazard. The stochastic properties of inrushes are related to the statistical properties of fissures in the karstic rock. The probability distributions (DF) of five random variables of interest in design are estimated; namely, inrush yield q, number N of inrushes per unit area, distance L between inrushes, maximum q_max in N events and total yield Q. The phenomenological hypotheses of log normal DF of q and Poisson DF of N are reinforced by observation data. On the basis of these DF, a Monte Carlo simulation of a spatial Poisson process of inrushes is run to estimate the DF of q_max and Q. The derivation of Bayesian DF to account for parameter uncertainty is discussed. The stochastic model is used for design and operation of minewater control facilities in the Transdanubian karstic region of Hungary.