A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments

Interactive genetic algorithm for user-centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

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

DEVELOPMENT OF A DECISION SUPPORT SYSTEM WEBTOOL FOR HISTORIC AND FUTURE LOW FLOW ESTIMATION IN THE NORTHEAST UNITED STATES WITH APPLICATIONS OF MACHINE LEARNING FOR ADVANCING PHYSICAL AND STATISTICAL METHODOLOGIES

Droughts are a global challenge and anthropogenic climate change is expected to increase the frequency and severity of extreme low flow events. A major challenge for resource managers is how best to incorporate future climate change projections into low flow event estimations, especially in ungaged basins. Using both physically based hydrology models and statistical models, this dissertation contributes novel methodologies to three key challenges associated with 7-day, 10-year low flow (7Q10) estimation in the northeast United States. Chapter 2 builds upon statistically based 7Q10 estimation in ungaged basins by comparing multiple machine learning algorithms to classical statistical methodologies. This chapter’s objective is to identify a robust statistical methodology applicable for the entire northeast U.S. that includes statistically significant climate variables that allow for the incorporation of climate change. Results suggest that the random forest method can provide regional 7Q10 estimates with similar errors to current, state-by-state 7Q10 estimates. Chapter 3 tests the applicability of a novel machine learning algorithm, Fuzzy C-Means clustering, to calibrate rainfall-runoff models in ungaged basins for both daily streamflow and 7Q10 estimation. Future updates to national rainfall-runoff models, which can directly incorporate climate change projections into calculations, will allow these models to be created in ungaged basins, but they will require extensive calibration and/or verification. Results suggest that this methodology significantly improves daily streamflow estimation but fails to improve 7Q10 estimation. Chapter 4 summarizes the development of a stakeholder-driven decision support system (DSS) web-application for calculating the 7Q10 at gages and estimating the 7Q10 in ungaged basins with projected climate changes. By incorporating the statistical model from Chapter 2 into the DSS and comparing the results to the physical modeling from Chapter 3, the DSS can estimate the impact of future temperature and precipitation changes on 7Q10s. This work highlights advancements in physical and statistical modeling techniques for 7Q10 estimation in ungaged basins and assists resource managers in addressing a growing need for incorporating anticipated climate change into drought calculations

Interactive Genetic Algorithms for watershed planning : an investigation of usability and human-centered design

Degradation of watersheds is a major concern in areas where adverse climate effects and unsustainable use of the natural resources have caused extensive stresses to watershed systems (e.g., increased floods, increased droughts, worsened in-stream water quality) through the years. While considerable efforts are being made to generate technical solutions that focus on plans of spatially-distributed conservation practices (e.g., Wetlands, Filter Strips, Grassed Waterways, Crop Management practices, etc.) for restoration of existing conditions in the watersheds, adoption and implementation of these solutions require a better understanding of constraints faced by affected stakeholders and decision makers. Participatory modeling and design approaches have, as a result, become popular in the recent past to support a community's engagement during the modeling process and during development of potential scenarios of plans (or, design alternatives). And now, with new and ongoing developments in Web 2.0 technologies, there is an even greater need for research that examines how large number of stakeholders can be engaged in the development of design alternatives via the internet-based, decision support environments. The overarching goal of this research is to investigate how stakeholder participation ("humans") and Interactive Genetic Algorithms ("computer") can be coupled in a web-based watershed decision support system (DSS) called WRESTORE (Watershed REstoration using Spatio Temporal Optimization of REsources- http://wrestore.iupui.edu/), in order to generate user-preferred design alternatives of distributed conservation practices on a watershed landscape. An important component of this goal is to also improve the understanding of how human behavior on the graphical user interface (GUI) of the DSS can be observed and evaluated in real-time, and then learned from to further improve the performance of the underlying search algorithm. Four specific objectives were addressed in this work to accomplish the overall goal: • Objective 1: Observe interactions of multiple users with the GUI of a web-based watershed DSS (WRESTORE, http://wrestore.iupui.edu/) during interactive search experiments, and then use Usability metrics (response times, clicking events and confidence levels) to evaluate the differences and similarities in user behaviors and interactions. • Objective 2: Examine relationships between the type of users (e.g., stakeholders versus surrogates), the Usability metrics, and patterns in the watershed-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. • Objective 3: Examine relationships between the type of users, the Usability metrics, and patterns in the user-preferred, sub-basin-scale plans of conservation practices generated by the multi-objective Interactive Genetic Algorithm embedded in WRESTORE. • Objective 4: Develop and test novel human-guided search operators that adaptively learn for patterns in user-preferred alternatives generated by the multi-objective Interactive Genetic Algorithm, and, as a result, improve the convergence rate of the search algorithm for generating design alternatives that conserve these learned patterns. Results show that there is a clear difference on how different types of users interact with the Interactive Optimization system. The observed relationship between confidence levels, time spent on a task, and number of mouse clicking events, indicated that participants who were able to use the WRESTORE GUI to gather more information and had a higher rate of time per number of clicks, tended to increase their levels of self-confidence in their own feedback. Also, when engaging with watershed stakeholders versus non-stakeholders (or, surrogates), 67% of the stakeholder participants steadily increased their average self-confidence levels as they continued to interact with the tool, in contrast to only 29% of surrogate participants who also showed an increase in their self-confidence levels through time. Such usability and confidence level evaluations provide assessments on which participant was potentially generating reliable feedback data for the search algorithm to use. An analysis of design alternatives generated by the individuals in both stakeholder and non-stakeholder groups showed that a majority (67%) of the stakeholder participants found a higher percentage (on and average 52%) of preferred design alternatives via the interactive search process. Also, users who were focused on assessing the suitability of design alternatives for the entire watershed trended to demonstrate a bias for one of the watershed-scale objective functions. In contrast, users, who were focused on assessing the suitability of design alternatives at only a few local sub-basins in the watershed, did not demonstrate any clear bias for any one of the watershed-scale objective functions. Additionally, patterns were observed in the design of decision alternatives generated by the human-centered search process, which further divulged potential user preferences related to the decision space for example, whether a specific participant preferred a certain practice over another, or a certain location over another for a specific practice. Finally, to improve the convergence rates of the Interactive Genetic Algorithm in WRESTORE, we investigated whether observed patterns in decisions (especially, when users were focused on local sub-regions of the watershed) can be used to improve the search for user-desire designs. A novel Interactive Genetic Algorithm with adaptive, human-guided, selection, crossover and mutation operators was proposed. The new algorithm was tested with six types of simulated participants (three deterministic and three probabilistic users) developed from the feedback data of three real participants. Results of search experiments with the novel adaptive IGA operators indicated a faster convergence than the default IGA, for two out of three deterministic simulated users. However, none of the probabilistic user showed a convergence different than the default values. This indicates that while current results indicate promise, there is need for additional research on adaptive, human-guided IGA operators, especially when noisy/stochastic users participate in the search. Additionally, adaptation of search operators have the potential to improve convergence rates when participatory design is done via Interactive Genetic Algorithms

A Transdisciplinary Approach to Decision Support for Dams in the Northeastern U.S. with Hydropower Potential

The Federal Energy Regulatory Commission (FERC) is the regulatory body that oversees non-federally owned dam operations in the United States. With more than 300 hydropower dams across the U.S. seeking FERC relicense between 2020 and 2029, and 135 of those dams within the Northeast region alone, it is prudent to anticipate and plan for such decision-making processes. Anyone may be involved in FERC relicensing; in fact, FERC solicits public comment and requires the licensee to hold a public hearing during the process. Parties may also elect to apply for legal intervenor status, allowing them a more formal entry into the relicensing process. However, there are two key barriers that may keep the public from participating in a dam decision-making process in an impactful way. The first of these barriers is access to information. Having access to the types of information that matters to FERC is important, because it allows the participant to communicate their support or concerns about the relicensing using the language of the process. In particular, participants other than the licensee may not have access to project economic information, so this is a focus in my research. The second barrier is capacity to participate in a way that impacts the process (i.e., institutional knowledge about what kinds of decision criteria (factors) and decision alternatives (project options), as well as relevant data, that FERC typically weighs in their decision making or has considered in the past). Actors not privy to license information (perhaps encountering difficulty in navigating the FERC eLibrary), lacking knowledge of FERC process conventions, or otherwise unfamiliar with hydropower dam schemes or operations have substantial hurdles preventing their effective participation. My research, situated in the sustainability science arena, addresses hydropower project cost and performance assessment and multi-criteria considerations for dam decision support. I lead the development and assessment of an online Dam Decision Support Tool aimed at addressing barriers to the hydropower dam decision-making process. My work demonstrates possibilities for tailoring decision tools to incorporate stakeholder perspectives into decision making about hydropower dams

Harmonizing Water Resource Management with Indigenous Ways of Knowing

Increases in the global population and accompanying demands for water and food production are having detrimental impacts on the sustainability of freshwater systems. These impacts include reduced water quality, abnormal flow fluctuations, and changes in sediment transport by water, among others. Another stressor on watersheds is climate change, as it is for all sensitive ecosystems. The Saskatchewan River Delta (SRD) is no exception. Populations in the SRD, such as the Indigenous communities in Cumberland House, have been adversely affected by upstream water withdrawals for irrigation, dam-induced alterations of the seasonal river flows for hydropower, and legacies of industrial pollution. Although research has demonstrated these and other problems, to date the perspective of the Cumberland House community has been inadequately considered in water resources modeling efforts and flow management. Consequently, the residents of the Delta have seen little in the way of adaptations and solutions. In this project, I sought to inform water resources and environmental modeling processes and practitioners with the values, insights, and perspectives of how altered water resource management in the SRD have changed from the point of view of the people of Cumberland House, so that developing models representing the Delta may better reflect local contextual factors in their execution. To achieve this objective, I used on-land participant observations and semi-structured interviews as a decolonizing tool to co-gather and analyze community members’ narratives on the issues in their environments. The results of this research identified and consolidated how the altered flows are affecting the Saskatchewan River Delta’s ecosystem and resident human and animal populations in terms of seasonality, livelihood, spiritual and cultural practices, and aesthetics. This research was completed within a community-engaged scholarship (CES) framework, which brought attention to issues in SRD communities, enhanced voice and agency of SRD residents, and paved the way for future knowledge incorporation not only in the SRD but also in other parts of the world, where interdisciplinary approaches to environmental sciences could lead to more vibrant and sustainable ecosystems

Flood Early Warning and Risk Modelling

Extreme hydrological phenomena are one of the most common causes of human life loss and material damage as a result of the manifestation of natural hazards around human communities. Climatic changes have directly impacted the temporal distribution of previously known flood events, inducing significantly increased frequency rates as well as manifestation intensities. Understanding the occurrence and manifestation behavior of flood risk as well as identifying the most common time intervals during which there is a greater probability of flood occurrence should be a subject of social priority, given the potential casualties and damage involved. However, considering the numerous flood analysis models that have been currently developed, this phenomenon has not yet been fully comprehended due to the numerous technical challenges that have arisen. These challenges can range from lack of measured field data to difficulties in integrating spatial layers of different scales as well as other potential digital restrictions.The aim of the current book is to promote publications that address flood analysis and apply some of the most novel inundation prediction models, as well as various hydrological risk simulations related to floods, that will enhance the current state of knowledge in the field as well as lead toward a better understanding of flood risk modeling. Furthermore, in the current book, the temporal aspect of flood propagation, including alert times, warning systems, flood time distribution cartographic material, and the numerous parameters involved in flood risk modeling, are discussed

Colorado water, September/October 2016

The newsletter is devoted to highlighting water research and activities at CSU and throughout Colorado.Newsletter of the Colorado Water Center. Theme: Reaching higher: implementing the Colorado Water Plan's goals for stream management

Implementing Nature-based Solutions and Green Infrastructure for Cities, Citizens and Rivers - The SEE-URBAN-WATER Project

Cities and their rivers are undergoing significant transformations owing to the impact of multiples challenges at a time such as rapid population growth, infrastructure development, and climate change. The consequences are evident in increased flood risks, groundwater pollution, accelerated soil erosion, drinking water scarcity, green space depletion, and biodiversity loss. In light of this, interest in novel concepts such as Nature-Based Solutions (NbS) is growing, extending beyond academia to influence micro-, meso-, and macro-urban scales. Motivated by the potential of NbS to deliver social, ecological, and societal benefits, the SEE-URBAN-WATER (SUW) research group aimed to provide a robust knowledge and methodological basis for achieving socio-ecological transformation through the inter- and transdisciplinary planning, design, and implementation of NbS and Green Infrastructures in highly urbanized areas susceptible to environmental and climate risks. From 2018 to 2023, SUW, funded within the framework of the Research for Sustainability program (known by its German acronym FONA) by the German Federal Ministry of Education and Research (abbreviated to BMBF in German), produced numerous master’s and doctoral theses, methodological frameworks, scientific publications, and technical guidelines. Nevertheless, this book goes beyond being a mere compendium of these outcomes; it clearly illustrates the systematic inter- and transdisciplinary evolution and interconnection of ideas for building more socially and environmentally resilient cities