In multiple watershed planning and design problems, such as conservation planning, quantitative estimates of costs, and environmental benefits of proposed conservation decisions may not be the only criteria that influence stakeholders' preferences for those decisions. Their preferences may also be influenced by the conservation decision itself—specifically, the type of practice, where it is being proposed, existing biases, and previous experiences with the practice. While human‐in‐the‐loop type search techniques, such as Interactive Genetic Algorithms (IGA), provide opportunities for stakeholders to incorporate their preferences in the design of alternatives, examination of user‐preferred conservation design alternatives for patterns in Decision Space can provide insights into which local decisions have higher or lower agreement among stakeholders. In this paper, we explore and compare spatial patterns in conservation decisions (specifically involving cover crops and filter strips) within design alternatives generated by IGA and noninteractive GA. Methods for comparing patterns include nonvisual as well as visualization approaches, including a novel visual analytics technique. Results for the study site show that user‐preferred designs generated by all participants had strong bias for cover crops in a majority (50%–83%) of the subbasins. Further, exploration with heat maps visualization indicate that IGA‐based search yielded very different spatial patterns of user‐preferred decisions in subbasins in comparison to decisions within design alternatives that were generated without the human‐in‐the‐loop. Finally, the proposed coincident‐nodes, multiedge graph visualization was helpful in visualizing disagreement among participants in local subbasin scale decisions, and for visualizing spatial patterns in local subbasin scale costs and benefits.

Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives.

Stream temperature is one of the most important factors for regulating fish behavior and habitat. Therefore, models that seek to characterize stream temperatures, and predict their changes due to landscape and climatic changes, are extremely important. In this study, we extend a mechanistic stream temperature model within the Soil and Water Assessment Tool (SWAT) by explicitly incorporating radiative flux components to more realistically account for radiative heat exchange. The extended stream temperature model is particularly useful for simulating the impacts of landscape and land use change on stream temperatures using SWAT. The extended model is tested for the Marys River, a western tributary of the Willamette River in Oregon. The results are compared with observed stream temperatures, as well as previous model estimates (without radiative components), for different spatial locations within the Marys River watershed. The results show that the radiative stream temperature model is able to simulate increased stream temperatures in agricultural sub-basins compared with forested sub-basins, reflecting observed data. However, the effect is overestimated, and more noise is generated in the radiative model due to the inclusion of highly variable radiative forcing components. The model works at a daily time step, and further research should investigate modeling at hourly timesteps to further improve the temporal resolution of the model. In addition, other watersheds should be tested to improve and validate the model in different climates, landscapes, and land use regimes.