North Gujarat in India currently extracts three billion cubic meters of groundwater per year, which is up to 95% of the groundwater resources available in the region. This unsustainable abstraction has led to changes in groundwater levels and created water scarcity in many parts of the region. To address these issues, integrated groundwater resource management is required, which should be driven by good quality and quantity of groundwater data. However, current groundwater data are scarce; thus, new, affordable monitoring approaches are necessary. Participatory and community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring. This study demonstrates the feasibility of developing a large-scale groundwater level monitoring wells network by directly involving farmers in two agriculturally-dominated blocks in North Gujarat, India. First, long-term groundwater level data for government-monitored wells were analyzed, and the regions lacking monitoring were identified. Then a network of 43 farmers was established through the field survey, who were trained to provide groundwater level observations for their wells every month. The data collected through the field survey were then integrated with the data from the existing government monitoring programs to understand the groundwater dynamics in the region. Results for the post-monsoon season 2022 show that the groundwater levels in Unjha block (Mehsana district) have declined to more than 100 meters below ground level due to unsustainable pumping for irrigation. The evaluation of the participatory approach showed that concern for existing groundwater challenges, social inclusion and contribution to scientific knowledge were the top three reasons that motivated farmers to participate in this research. Of the total volunteering farmers, 71% have shown interest in providing long-term observations for up to 3 years, and 57% agreed to provide observations weekly. Additionally, 70% of the farmers agreed to engage fellow farmers in groundwater monitoring, and 50% agreed to train new farmers. Thus, this study shows that farmers can play an important role in improving the existing challenges of groundwater monitoring through participatory training, and the integration of primary and secondary data can lead to better decision-making regarding need for well construction, crop selection, recharge methods and pathways for sustainable groundwater management.

Indigenous communities in Canada are disproportionately affected by issues related to water security, especially access to clean water to meet human needs. The issues these communities face are diverse and widespread across Canada, with many causes and consequences. This review summarizes the types and magnitudes of risks associated with the water security of these communities, the consequences considering health and social perspectives, and the means of responding to these issues. Risks are broadly divided into quantitative risks (e.g., water quality and availability) and qualitative risks (e.g., lack of funding and jurisdictional conflicts). These risks lead to unique consequences, resulting in challenges in developing generalized risk response frameworks. Management of these risks includes a mix of techniques relying on legislative and technical approaches. Nevertheless, the affected communities should be included in the decision-making process that should be holistic, incorporating indigenous knowledge. Good governance, cooperation between communities, policy improvement and the development of an institutional mechanism for clean water supply will provide a pathway and guidelines to address the water security challenges among indigenous communities.

Strategies are proposed to cope with uncertainties in a way that all possible kinds of uncertainty are named, recognized, statistically quantified as far as possible and utilized in efficient decision-making in flood risk management (FRM). We elaborated on the metaphor of uncertainty as a monster. We recommend two strategies to cope with the uncertainty monster to support efficient decision-making in FRM: monster adaptation and monster assimilation. We present three cases to illustrate these strategies. We argue that these strategies benefit from improving the structure and reducing the complexity of decision problems. We discuss ways to involve decision-makers in FRM, and how communication strategies can be responsive to their informational needs.

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the model-centric approach. These endeavors can certainly pave the way for more tailor-fitted models capable of producing accurate results. However, such a perspective often neglects a fundamental assumption of these models, which is the importance of reliability, correctness, and accessibility of the data used in constructing them. This challenge arises from the prevalent model-centric paradigm of thinking in the field. An alternative approach, however, would prioritize placing data at the focal point, focusing on systematically enhancing current datasets and devising frameworks to improve data collection schemes. This suggests a paradigm shift toward more data-centric thinking in water and environmental engineering. Practically, this shift is not without challenges and necessitates smarter data collection rather than an excessive one. Equally important is the ethical and accurate collection of data, making it available to everyone while safeguarding the rights of individuals and other legal entities involved in the process.