Bioeconomic models are analytical tools that integrate biophysical and economic models. These models allow for analysis of the biological and economic changes caused by human activities. The biophysical and economic components of these models are developed based on historical observations or theoretical relations. Technically these models may have various levels of complexity in terms of equation systems considered in the model, modeling activities, and programming languages. Often, biophysical components of the models include crop or hydrological models. The core economic components of these models are optimization or simulation models established according to neoclassical economic theories. The models are often developed at farm, country, and global scales, and are used in various fields, including agriculture, fisheries, forestry, and environmental sectors. Bioeconomic models are commonly used in research on environmental externalities associated with policy reforms and technological modernization, including climate change impact analysis, and also explore the negative consequences of global warming. A large number of studies and reports on bioeconomic models exist, yet there is a lack of studies describing the multiple uses of these models across different disciplines.
Water Resources Planning Under (Deep) Uncertainty
Public investments in water infrastructure continue to grow where developed countries prioritize investments in operation and maintenance while developing countries focus on infrastructure expansion. The returns from these investments are contingent on carefully assessed designs and operating strategies that consider the complexities inherent in water management problems. These complexities arise due to several factors, including, but not limited to, the presence of multiple stakeholders with potentially conflicting preferences, lack of knowledge about appropriate systems models or parameterizations, and large uncertainties regarding the evolution of future conditions that will confront these projects. The water resources planning literature has therefore developed a variety of approaches for a quantitative treatment of planning problems. Beginning in the mid-20th century, quantitative design evaluations were based on a stochastic treatment of uncertainty using probability distributions to determine expected costs or risk of failure. Several simulation–optimization frameworks were developed to identify optimal designs with techniques such as linear programming, dynamic programming, stochastic dynamic programming, and evolutionary algorithms. Uncertainty was incorporated within existing frameworks using probability theory, using fuzzy theory to represent ambiguity, or via scenario analysis to represent discrete possibilities for the future. As the effects of climate change became palpable and rapid socioeconomic transformations emerged as the norm, it became evident that existing techniques were not likely to yield reliable designs. The conditions under which an optimal design is developed and tested may differ significantly from those that it will face during its lifetime. These uncertainties, wherein the analyst cannot identify the distributional forms of parameters or the models and forcing variables, are termed “deep uncertainties.” The concept of “robustness” was introduced around the 1980s to identify designs that trade off optimality with reduced sensitivity to such assumptions. However, it was not until the 21st century that robustness analysis became mainstream in water resource planning literature and robustness definitions were expanded to include preferences of multiple actors and sectors as well as their risk attitudes. Decision analytical frameworks that focused on robustness evaluations included robust decision-making, decision scaling, multi-objective robust decision-making, info-gap theory, and so forth. A complementary set of approaches focused on dynamic planning that allowed designs to respond to new information over time. Examples included adaptive policymaking, dynamic adaptive policy pathways, and engineering options analysis, among others. These novel frameworks provide a posteriori decision support to planners aiding in the design of water resources projects under deep uncertainties.
A Century of Evolution of Modeling for River Basin Planning to the Next Generation of Models, Methods, and Concepts
Caroline Rosello, Sondoss Elsawah, Joseph Guillaume, and Anthony Jakeman
River Basin models to inform planning decisions have continued to evolve, largely based on predominant planning paradigms and progress in the sciences and technology. From the Industrial Revolution to the first quarter of the 21st century, such modeling tools have shifted from supporting water resources development to integrated and adaptive water resources management. To account for the increasing complexity and uncertainty associated with the relevant socioecological systems in which planning should be embedded, river basin models have shifted from a supply development focus during the 19th century to include, by thes 2000s–2020s, demand management approaches and all aspects of consumptive and non-consumptive uses, addressing sociocultural and environmental issues. With technological and scientific developments, the modeling has become increasingly quantitative, integrated and interdisciplinary, attempting to capture, more holistically, multiple river basin issues, relevant cross-sectoral policy influences, and disciplinary perspectives. Additionally, in acknowledging the conflicts around ecological degradation and human impacts associated with intensive water resource developments, the modeling has matured to embrace the need for adequate stakeholder engagement processes that support knowledge-sharing and trust-building and facilitate the appreciation of trade-offs across multiple types of impacts and associated uncertainties. River basin models are now evolving to anticipate uncertainty around plausible alternative futures such as climate change and rapid sociotechnical transformations. The associated modeling now embraces the challenge of shifting from predictive to exploratory tools to support learning and reflection and better inform adaptive management and planning. Managing so-called deep uncertainty presents new challenges for river basin modeling associated with imperfect knowledge, integrating sociotechnical scales, regime shifts and human factors, and enabling collaborative modeling, infrastructure support, and management systems.