Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers

Common pool resource management systems are complex to manage due to the absence of a clear understanding of the effects of users’ behavioral characteristics. Non-cooperative decision making based on individual rationality (as opposed to group rationality) and a tendency to free ride due to lack of trust and information about other users’ behavior creates externalities and can lead to tragedy of the commons without intervention by a regulator. Nevertheless, even regulatory institutions often fail to sustain natural common pool resources in the absence of clear understanding of the responses of multiple heterogeneous decision makers to different regulation schemes. While modeling can help with our understanding of complex coupled human-natural systems, past research has not been able to realistically simulate these systems for two major limitations: 1) lack of computational capacity and proper mathematical models for solving distributed systems with self-optimizing agents; and 2) lack of enough information about users’ characteristics in common pool resource systems due to absence of reliable monitoring information. Recently, different studies have tried to address the first limitation by developing agent-based models, which can be appropriately handled with today’s computational capacity. While these models are more realistic than the social planner’s models which have been traditionally used in the field, they normally rely on different heuristics for characterizing users’ behavior and incorporating heterogeneity. This work is a step-forward in addressing the second limitation, suggesting an efficient method for collecting information on diverse behavioral characteristics of real agents for incorporation in distributed agent-based models. Gaming in interactive virtual environments is suggested as a reliable method for understanding different variables that promote sustainable resource use through observation of decision making and iii behavior of the resource system beneficiaries under various institutional frameworks and policies. A review of educational or "serious" games for environmental management was undertaken to determine an appropriate game for collecting information on real-agents and also to investigate the state of environmental management games and their potential as an educational tool. A web-based groundwater sharing simulation game—Irrigania—was selected to analyze the behavior of real agents under different common pool resource management institutions. Participants included graduate and undergraduate students from the University of Central Florida and Lund University. Information was collected on participants’ resource use, behavior and mindset under different institutional settings through observation and discussion with participants. Preliminary use of water resources gaming suggests communication, cooperation, information disclosure, trust, credibility and social learning between beneficiaries as factors promoting a shift towards sustainable resource use. Additionally, Irrigania was determined to be an effective tool for complementing traditional lecture-based teaching of complex concepts related to sustainable natural resource management. The different behavioral groups identified in the study can be used for improved simulation of multi-agent groundwater management systems.

The impact of forest fires on downstream meadow communities across California is of great ecological interest, as meadows are an important source of biodiversity in this region. Over a century of fire suppression has led to increased forest stand densities, which in turn has resulted in less water availability due to increased transpiration of densely growing trees. This potentially has left less available water for downstream plant communities in meadows. If true, then high mortality wildfires in surrounding forest are predicted to lead to an increase in available downstream moisture where obligate and facultative-wetland taxa increase and dry-adapted upland taxa decline. Here, we test this hypothesis using a dataset of 103 California montane meadows sampled before and after fire over the last 20 years. Using long term meadow monitoring data, compositional turnover is calculated for each plot from before and after fire and then evaluated against the area of 100% mortality, postfire relative-precipitation, meadow type, and proximity of the meadow to fire. We hypothesize that mortality, post-fire precipitation, and site type influence compositional turnover in meadows, regardless of proximity to the burn area. We find that compositional turnover is influenced by mortality but not by meadow type, relative precipitation, or the proximity to fire perimeter. Specifically, turnover was greater in meadows in higher mortality catchments. We then used a combination of linear models and NMDS to determine whether specific functional groups were driving higher turnover rates, expecting increases in obligate and facultative-wetland groups following high mortality fires. However we found no evidence for this. The high variation amongst meadows and their respective fire histories yielded no consistent shifts in community composition. Our findings highlight that landscape scale fire effects can interact strongly affect plant communities outside of fire perimeters, but that this does not lead to predictable shifts in wetland community composition. As fire behavior and drought are projected to become more extreme, we can expect that meadow composition will continue to change but not in predictable ways.

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Pharmaceutical drugs are being produced and consumed in increasing quantities every year and are poorly treated by conventional wastewater treatment processes, leading to increasing detection of such compounds in surface water, groundwater, and municipal drinking water. Soil aquifer treatment (SAT) is a promising method for treating these emerging compounds through combined adsorption and degradation of target compounds in soil. This thesis examines the consistency of results from typical studies like adsorption isotherms and soil columns utilized in analysis of SAT performance, across varying experimental scales. The adsorption behavior of two pharmaceuticals was investigated as a function of experimental scale and soil organic content in adsorbent media. This thesis shows that broad trends in pharmaceutical adsorption are not dependent upon experimental scale. Across adsorption isotherm, bench-scale soil column, and large-scale soil column experiments, adsorption and of both drugs was greater in organic soil than inorganic soil, although dispersive transport may have increased in some experiments. Across all experiments, carbamazepine was adsorbed by soils more than diclofenac. Some inconsistencies were observed across scales between the two organic soils, a mediumorganic and high-organic soil, where adsorption was generally greater in high organic soil, but sometimes observed to be greater in medium organic soil. This may suggest that the decrease of experimental control resulting from increased experimental scale obfuscates more nuanced relationships in SAT experimental conditions. Broad trends in data showing whether or not a soil displayed significant adsorptive behavior and which pharmaceutical was adsorbed more were consistent. However, the degree of partitioning via adsorption varied across scales as experimental control decreased with increasing physical scale.

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