Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

Abstract

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

Abstract

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Temperature simulation by numerical modeling and feedback of geostatic data and horizontal domain resolution

Abstract

The accuracy of the Weather Research and Forecasting Model (WRF) can be affected by multiple factors, including domain resolution, geostatic data, and model configuration. This study examined the sensitivity of simulated seasonal temperature to different geostatic data, horizontal domain resolutions, and configurations in Northeast Iran. During the investigation, the WRF model utilized Asymmetric Convective Model version 2 (ACM2) planetary boundary layer, WRF-single-moment-microphysics classes 6 (WSM6) Microphysic, Geophysical Fluid Dynamics Laboratory (GFDL) Long-wave/short-wave radiation parameterization schemes, and Climate Forecast System version2 (CFSV2) initial and boundary conditions from Nov 2019 to Feb 2020. The default (States Geological Survey (USGS)/ Moderate Resolution Imaging Spectroradiometer (MODIS)) and high-resolution (ASTER/Copernicus) geostatic data and inner domain resolutions 3 and 6 km were set for model simulation. The results revealed that following the physical configuration, the model simulation’s highest sensitivity was associated with the domain resolution and geostatic data. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) had approximately similar results in the 6 km domain for both geostatic data, but the Mean Bias (MB) showed a cold Bias. The MB results were warmer when the horizontal resolution increased from 6 to 3 km. To obtain reliable temperature simulation, WRF was more sensitive to horizontal domain resolution than geostatic data. However, the accuracy of geostatic data affected the distribution of temperature patterns. A greater error appeared in the lower horizontal domain resolution (6 km) and low-resolution geostatic data (default), especially in complex terrains.

Temperature simulation by numerical modeling and feedback of geostatic data and horizontal domain resolution

Abstract

The accuracy of the Weather Research and Forecasting Model (WRF) can be affected by multiple factors, including domain resolution, geostatic data, and model configuration. This study examined the sensitivity of simulated seasonal temperature to different geostatic data, horizontal domain resolutions, and configurations in Northeast Iran. During the investigation, the WRF model utilized Asymmetric Convective Model version 2 (ACM2) planetary boundary layer, WRF-single-moment-microphysics classes 6 (WSM6) Microphysic, Geophysical Fluid Dynamics Laboratory (GFDL) Long-wave/short-wave radiation parameterization schemes, and Climate Forecast System version2 (CFSV2) initial and boundary conditions from Nov 2019 to Feb 2020. The default (States Geological Survey (USGS)/ Moderate Resolution Imaging Spectroradiometer (MODIS)) and high-resolution (ASTER/Copernicus) geostatic data and inner domain resolutions 3 and 6 km were set for model simulation. The results revealed that following the physical configuration, the model simulation’s highest sensitivity was associated with the domain resolution and geostatic data. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) had approximately similar results in the 6 km domain for both geostatic data, but the Mean Bias (MB) showed a cold Bias. The MB results were warmer when the horizontal resolution increased from 6 to 3 km. To obtain reliable temperature simulation, WRF was more sensitive to horizontal domain resolution than geostatic data. However, the accuracy of geostatic data affected the distribution of temperature patterns. A greater error appeared in the lower horizontal domain resolution (6 km) and low-resolution geostatic data (default), especially in complex terrains.

Hemispheric asymmetric response of tropical cyclones to CO2 emission reduction

Abstract

Tropical cyclones (TCs) are among the most devastating natural hazards for coastal regions, and their response to human activities has broad socio-economic relevance. So far, how TC responds to climate change mitigation remains unknown, complicating the design of adaptation policies. Using net-zero and negative carbon emission experiments, we reveal a robust hemisphere-asymmetric hysteretic TC response to CO2 reduction. During the decarbonization phase, the Northern Hemisphere TC frequency continues to decrease for several more decades, while the Southern Hemisphere oceans abruptly shifts to a stormier state, with the timescales depending on mitigation details. Such systematic changes are largely attributed to the planetary-scale reorganization of vertical wind shear and midlevel upward motion associated with the hysteretic southward migration of the Intertropical Convergence Zone, underpinned by the Atlantic Meridional Overturning Circulation and El Niño-like mean state changes. The hemispheric contrast in TC response suggests promising benefits for most of the world’s population from human action to mitigate greenhouse gas warming, but it may also exacerbate regional socioeconomic disparities, for example by putting more pressure on small open-ocean island states in the Southern Hemisphere to adapt to TC risks.

Hemispheric asymmetric response of tropical cyclones to CO2 emission reduction

Abstract

Tropical cyclones (TCs) are among the most devastating natural hazards for coastal regions, and their response to human activities has broad socio-economic relevance. So far, how TC responds to climate change mitigation remains unknown, complicating the design of adaptation policies. Using net-zero and negative carbon emission experiments, we reveal a robust hemisphere-asymmetric hysteretic TC response to CO2 reduction. During the decarbonization phase, the Northern Hemisphere TC frequency continues to decrease for several more decades, while the Southern Hemisphere oceans abruptly shifts to a stormier state, with the timescales depending on mitigation details. Such systematic changes are largely attributed to the planetary-scale reorganization of vertical wind shear and midlevel upward motion associated with the hysteretic southward migration of the Intertropical Convergence Zone, underpinned by the Atlantic Meridional Overturning Circulation and El Niño-like mean state changes. The hemispheric contrast in TC response suggests promising benefits for most of the world’s population from human action to mitigate greenhouse gas warming, but it may also exacerbate regional socioeconomic disparities, for example by putting more pressure on small open-ocean island states in the Southern Hemisphere to adapt to TC risks.

Appraising biocultural approaches to sustainability in the scientific literature in Spanish

Abstract

Biocultural approaches that acknowledge the multiple and dynamic relationships between the diversity of cultures and nature are growing in popularity in sustainability research. Scientific contributions to biocultural approaches written in Spanish are numerous, including influential work on biocultural memory, biocultural heritage and biocultural ethics. However, despite linguistic diversity being considered essential in knowledge production for assuring broad and balanced evidence to successfully cope with sustainability challenges, non-English literature is rarely reviewed and taken into account in English-language scientific knowledge production and publications. This review assesses how the scientific literature in Spanish conceptualizes and applies biocultural approaches, showing their richness beyond the Anglophone predominance in academic knowledge production and communication. The results suggest that insights from Spanish-language scientific literature could contribute alternative methodological and theoretical pathways for biocultural approaches that might foster transformations for more sustainable human-nature relationships. We conclude by highlighting avenues that could bring more plural biocultural studies.

Claiming ecological grief: Why are we not mourning (more and more publicly) for ecological destruction?

Abstract

Eco-anxiety, grief and despair are increasing, yet these emotions tend to remain private, rarely expressed in public. Why is it important and necessary to grieve for ecological loss? Why are we not—as individuals and societies—coming together to express and share our grief for ecological destruction? I address these questions from three angles. Firstly, I draw on recent literature on ecological grief and prior work on grief for human lives, to argue for the importance and urgency of grieving publicly for ecological loss. Building on this, I identify perceptual, cognitive, affective, ritual and political obstacles to ecological mourning; these obstacles point at critical intersections between emotions, practices, disciplines, public and private realms, which can turn into fruitful venues for further research, debate and action on ecological grief (and its absence). In closing, I propose a set of ‘ecological skills’ that might help us overcome these obstacles, and lead us to embrace ecological grief and mourning as acts of ethical responsibility and care for the planet.

Impact of the COVID-19 Pandemic on People Living with Rheumatoid Arthritis: Experiences and Preferences in Accessing Healthcare Across Five Countries

Abstract

Introduction

The global coronavirus 2019 (COVID-19) pandemic created many challenges in healthcare provision. This study aimed to evaluate the global impact of the COVID-19 pandemic on people living with rheumatoid arthritis (RA).

Methods

The RA Narrative COVID-19 survey was conducted online among people with RA who resided in Brazil, Canada, France, Japan, and the US from August to September 2021. The survey examined disease management, healthcare access and experiences, and participant preferences for interactions with their doctor.

Results

Overall, 500 participants completed the survey: 100 each resided in Brazil, Canada, France, Japan, and the US. Emotional well-being was the aspect of disease management most reported to be negatively impacted by the pandemic (55% of participants); ‘having more anxiety and/or stress’ during the pandemic was the top factor that made controlling RA symptoms more difficult (49% of participants). In comparison, the top factor that made controlling RA symptoms easier was ‘having a less busy schedule’ (35% of participants). More participants had virtual appointments during versus pre-pandemic (53% vs. 13%, respectively) and participants were equally satisfied with the overall quality of care received via virtual and in-person appointments (76% of participants were ‘satisfied’ or ‘very satisfied’ with both). However, participants generally preferred in-person over virtual appointments, except for prescription refills, for which preferences were similar (39% vs. 36%, respectively).

Conclusions

This survey suggests that the COVID-19 pandemic did negatively impact some aspects of disease management for people living with RA but had positive impacts on the utilization of virtual care. Although participants generally preferred in-person appointments, these results position virtual care as an appropriate means for routine follow-ups.

Wapítat Ttáwaxt (Community in Service to Community): Results of Community Stakeholder Engagement into the Initial Development of a Tribally Adapted Interdependent Life Skills Curriculum for American Indian Teens

Abstract

To engage tribal community members in adapting an evidence based, youth life skills curriculum for transition-age youth who have experienced multiple risk factors that prevent a healthy transition to adulthood. This exploratory, qualitative study draws on the voices of twelve critical community member (providers, parents & youth) that identified the specific cultural adaptations for the curriculum. Three primary overlapping themes emerged (1) the importance of incorporating AIAN identity and ceremony in the intervention design, (2) how the program should be integrated into current service delivery and (3) the specific curricular components to be delivered, including by whom and how they should be delivered. The inclusion of tribal members is key to building culturally relevant service delivery systems that will meet the needs of American Indian adolescents and young adults who are who are transitioning to adult hood in a American Indian community. Implications for policy and practice are offered.