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Cities will face increasing challenges due to the impacts of global climate change, particularly in the form of cyclonic events, necessitating a deeper understanding and the establishment of effective response mechanisms at both institutional and citizen levels. In this research, we tested the efficiency of crowdsourcing in fostering participatory resilience and improving urban management. The main aim was to design novel and accurate proactive response strategies and mitigate the adverse effects of cyclonic wind events through volunteerism, citizen science, and urban science. To achieve this goal, as a case study, the municipality of Soledad, Colombia was used. This research employed a two-phase methodological approach: (i) initially evaluating the spatial distribution of emergency response resources, and (ii) developing a geo-referenced survey to map, systematize, and categorize data and outcomes. A total of three hundred and seventy-eight residents across five neighborhoods in Soledad, which have experienced a high frequency of atmospheric wind phenomena over the past two decades, were surveyed. The results indicate that the crowdsourcing mechanism effectively enhanced the empirical understanding of atmospheric wind events in Soledad, facilitating the establishment of a geo-referenced volunteer network for real-time responses. Additionally, this study shed light on previously undocumented challenges, in terms of reducing the number of people affected, and the actions that would lead to improved urban development to reduce the impacts of cyclonic events, emphasizing the significance of citizen science in the social construction of risk and disaster risk reduction (DDR) efforts. [ABSTRACT FROM AUTHOR]

Climate change is modifying the spatiotemporal patterns of global precipitation events, temperatures, and winds, therefore, after extreme events, improving emergency responses in urban areas is key to saving its inhabitants. In Southern America, the number of extreme events is increasing. This is the case of Soledad, the municipality in Colombia where most of the catastrophic wind phenomena take place. To date, no studies have been conducted to quantify the impacts and effects of the urban cyclonic wind phenomena on society and the urban built environment. This lack of information and dissemination means that the population is not informed of the magnitude of the problem. This research aims to generate a risk map of atmospheric wind phenomena to evaluate their impacts and establish spatial-temporal correlations based on meteorological data from the last 20 years. Moreover, the online press has been used to identify the location of these phenomena and their negative impacts over time. For each event, the following indicators have been studied: (1) location of the atmospheric wind events; (2) occurrence rate; (3) impact of the events discriminated by fatalities, injuries, and affected houses. The results show that in 20 years, a total of 34 urban cyclonic wind events were reported. Those occurrences have impacted 60 neighbourhoods, leaving 7 deaths, 14,552 injured, and 5180 affected homes. These findings show the magnitude of the problem and the need to inform the population to improve emergency responses. We conclude that effective consideration of the resulting map will be crucial in the processes of decision-making related to territorial planning in Soledad, but also in other Southern American cities. • Risk map of atmospheric wind phenomena and their spatial-temporal correlations. • In 20 years, 34 urban cyclonic wind events: 14,552 injured, 5180 affected homes. • The resulting map will be crucial in the processes of territorial planning. [ABSTRACT FROM AUTHOR]

Rapid population growth and climate change are poised to significantly impact water resources and agriculture. Consequently, it becomes imperative to delve into the repercussions of climate change on agriculture and natural resources in developing nations. In this research, an economic–hydrological model was employed to assess the influence of climate change on water management and cropping patterns in the Eastern border catchment of Iran. The climatic data used in this study, spanning the years 1997–2022, encompassed daily rainfall and temperature records from the Khorasan and Sistan & Balouchestan meteorological organization, as well as the Iranian Statistical Center. Monthly water discharge data were obtained from the Khorasan and Sistan & Balouchestan regional water authority, while information on crop levels and agricultural inputs was collected from the Khorasan and Sistan & Balouchestan agricultural jihad organization. The findings reveal that areas dedicated to cultivating high-yield crops experienced a decrease when associated with high water consumption, whereas the cultivation areas of high-yield crops expanded when water consumption was reduced. The implementation of modern irrigation methods, such as pressurized irrigation, not only conserves valuable water resources but also enhances efficiency, ultimately resulting in decreased water consumption and increased production. Consequently, the provision of enhanced training programs aimed at educating farmers about these practices and facilitating their adoption can play a pivotal role in adapting to and mitigating the challenges posed by climate change. • Impact of Climate Change: Influence on water management and cropping patterns. • Data Sources: climatic data (1997–2022) from meteorological and water authorities. • Crop Area Dynamics: High-yield crop areas decreased with high water consumption while expanding with reduced consumption. • Irrigation Efficiency: to reduce water consumption and increase production. • Call for Action: Emphasis on farmer education and adoption of efficient practices. [ABSTRACT FROM AUTHOR]

Agricultural lands are sources and sinks of greenhouse gases (GHGs). The identification of the main drivers affecting GHGs is crucial for planning sustainable agronomic practices and mitigating global warming potential. The main aim of this research was to evaluate the impact of environmental drivers (soil temperature and water-filled pore space, WFPS) and crop residue rates on CO 2 , NO, and NO x fluxes under conventional tillage (CT) and no-tillage (NT) systems. The accuracy of Random Forest Regression (RFR), Multiple Adaptive Regression Splines (MARS), and General Linear Models (GLM) in predicting CO 2 , NO, and NO x fluxes were also assessed. In both CT and NT systems, CO 2 , NO, and NO x fluxes decreased with increasing WFPS. Increasing temperature resulted in higher CO 2 emissions and lower NO and NO x emissions. Higher residue rates resulted in significant increases in CO 2 emission, whereas the NO and NO x emissions increased by decreasing the ratio of residue. For CO 2 prediction, the RFR provided the largest R2 with the observed data. For NO–N and NO x -N prediction, RFR was the most efficient algorithm, but NO–N can be predicted with better accuracy. The output of this research highlights the importance of agronomic practices for climate mitigation, along with the possibility of using RFR to predict GHGs fluxes. • Increasing temperature increased CO 2 emissions and decreased NO and NO x emissions. • The highest NO and NO x emissions were observed in the residue removal treatment. • Increasing WFPS decreased CO 2 , NO, and NO x emissions. • The accuracy of the machine learning algorithm methods (RFR, MARS, and GLM) was evaluated. • RFR (among RFR, MARS, and GLM) had the highest accuracy in predicting emissions. [ABSTRACT FROM AUTHOR]