Author: Latest Results

Modeling of surface water allocation under current and future climate change in Keleta Catchment, Awash River Basin, Ethiopia

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Abstract

The water resources of Keleta catchment in the Awash River basin are utilized by various users. However, the current and future demands for water and its availability in the catchment have not been quantified. Therefore, this study aimed to evaluate the current and future water demand and availability by employing multiple climate models under the representative concentration pathways (RCP 4.5 and 8.5) scenarios. A power transformation method was applied for precipitation and linear shifting and scaling techniques were used for temperature to obtain bias-corrected future climate data. These bias-corrected daily precipitation and temperature datasets were utilized to simulate surface water availability for reference (1971–2000) and future climate scenarios (2041–2070) periods under RCP 4.5 and 8.5 using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model. The Water Evaluation and Planning (WEAP) model was employed to assess water allocation within the catchment.The HEC-HMS model simulation results revealed that the simulated hydrograph better captured the pattern of observed hydrograph for both calibration and validation periods. The results of the maximum and minimum temperature for the future period from 2041 to 2070 revealed increase on average by 1.62 °C and 1.43 °C for RCP 4.5, while by 2.26 °C and 1.71 °C for RCP 8.5, respectively. The average annual water availability and demand under current condition were found to be 247.4 million cubic meters (MCM) and 7.13 MCM, respectively. Future surface water availability is expected to increase by 23.9% under RCP 4.5 and 28.9% under RCP 8.5 compared to the refernce period. The WEAP simulation revealed monthly variations in water availability, highlighting unmet demand during the dry months of December to February due to reduced water availability during this period. This study suggests for integrated planning and management of the catchment area, particularly focusing on various water resource development activities, especially during dry seasons.

Optimizing climate model selection in regional studies using an adaptive weather type based framework: a case study for extreme heat in Belgium

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Abstract

Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, low-performing models are excluded. In the last step, indices are introduced that quantify the climate change signals related to the impact of interest. Using these indices, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. This framework offers a comprehensive method for selecting relevant climate projections, applicable in model ensemble-based research for various climate variables and impact drivers.

Communications enhance sustainable intentions despite other ongoing crises

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Abstract

There is an ongoing trend toward more frequent and multiple crises. While there is a clear need for behaviors to become more sustainable to address the climate crisis, how to achieve this against the backdrop of other crises is unknown. Using a sample of 18,805 participants from the UK, we performed a survey experiment to investigate if communication messages provide a useful tool in nudging intentions toward improved sustainability in the context of the COVID-19 pandemic. We found that, despite the ongoing COVID-19 crisis, media messaging resulted in increases in sustainability-related intentions for all our communication messaging conditions. Specifically, after our communication was presented, (i) almost 80% of people who were not currently recycling their surgical masks reported their intention to do so; there was a > 70% increase in both (ii) the number of people likely to pick up face mask litter and (iii) the number of people willing to disinfect and reuse their filtering facepiece (FFP) masks 4–6 times, while (iv) there was an increase by 165% in those who would wash cloth masks at 60 °C. Our results highlight that communication messaging can play a useful role in minimizing the trade-offs between multiple crises, as well as maximizing any synergies. To support this, decision-makers and practitioners should encourage the delivery of sustainability advice via multiple sources and across different types of media, while taking steps to address potential misinformation.

COVID-19 vaccine refusal is driven by deliberate ignorance and cognitive distortions

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Abstract

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.

High-resolution projections of future FWI conditions for Portugal according to CMIP6 future climate scenarios

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Abstract

Wildfires are catastrophes of natural origin or initiated by human activities with high disruptive potential. "Portugal, located in western Iberia, has recently experienced several large fire events, including megafires, due to a combination of factors such as orography, vegetation, climate, and socio-demographic conditions that contribute to fuel accumulation.". One approach to studying fire danger is to use fire weather indices that are commonly used to quantify meteorological conditions that can lead to fire ignition and spread. This study aims to provide high-resolution (~ 6 km) future projections of the Fire Weather Index (FWI) for Portugal using the Weather Research and Forecasting (WRF) model, forced by the Max Planck Institute (MPI) model from the CMIP6 suite, under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP58.5) for the present period (1995–2014) and two future periods (2046–2065 and 2081–2100). The results show good agreement between FWI and its subcomponents from the WRF and reanalysis. The modelled FWI reproduced the climatological distribution of fire danger Projections indicate an increase in days with very high to extreme fire danger (FWI > 38) across all scenarios and time frames, with the southern and northeastern regions experiencing the most significant changes. The southern and northeastern parts of the territory experienced the largest changes, indicating significant changes between the scenarios and regions. This study suggests that FWI and its subcomponents should be investigated further. Our results highlight the importance of creating new adaptation measures, especially in the areas most at risk, prepared in advance by different players and authorities, so that the increasing risk of wildfires can be mitigated in the future.

Convection Permitting Regional Climate Modelling Over the Carpathian Region

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Abstract

A preliminary analysis of the performance of the latest version of the RegCM regional modelling system, RegCM5, run at a convection permitting resolution (2 km) over the Carpathian Basin is presented for the following years: 1980, 2006, 2008 and 2010. The performance of the model is assessed using various statistics of surface air temperature and precipitation against the CARPATCLIM high-resolution observational dataset and the ERA5 reanalysis, which also provides the driving field for the simulations. While the model performs generally well, it exhibits a warm bias over the Hungarian lowlands during the warm season and a wet (dry) bias over the mountain chains (flat regions) within the basin. The model also shows a strong orographic forcing of precipitation. In general, RegCM5 has a systematic positive precipitation bias over mountainous regions, which can also be attributed to the relatively low station density of the observation network. The high-resolution model adds value especially for simulating medium to high-intensity precipitation events. Our preliminary experiments provide encouraging indications towards the applicability of RegCM5 to the Carpathian region. Future work will include testing the model with different physics configurations and longer simulations and applying the model to climate change studies over the Carpathian Basin.

Digital Transformations Through the Lens of the Collaborative, Co-Generative and Domesticative

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Abstract

We explore digital transformation and socio-technical systems through the perspectives of collaboration, co-generation, and domestication. Building on Morten Levin’s influential work, we discuss how digital technologies are integrated into work environments, emphasising the necessity for democratic, participatory approaches. We discuss how his ideas of collaborative, democratic practice originating in the 1990s hold up to today’s organisational challenges like digital and sustainability transitions. Are the ideas and practices still valid, or do we need to update them? We identify the grand challenges that must be met: Ensuring sustainable digital transitions and developing educated, skilled workers in digitally transformed organisations.

Monitoring Terrestrial Water Storage Using GRACE/GRACE-FO Data over India: A Review

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Abstract

The gravity recovery and climate experiment (GRACE) satellite mission, which was active between March 2002 and June 2017 and its successor, the GRACE follow-on (GRACE-FO), which has been in operation since May 2018, marked the pioneering remote sensing missions to track changes in terrestrial water storage (TWS) across time. TWS encompasses the cumulative water masses found in the Earth’s soil column, including elements like surface water, soil moisture, snow water equivalent and groundwater (GW). Over the course of the last 20 years, there has been extensive research conducted on fluctuations in the mass of different Elements of the Earth's system, such as the hydrosphere, seas, cryosphere, and solid Earth, utilizing time-varying gravity measurements from the GRACE/GRACE-FO missions. This technology can be utilised to improve monitoring results of large-scale spatial and temporal variations in the water cycle patterns. A review of recent GRACE data used for monitoring terrestrial hydrology over India is provided in this work. The primary applications of GRACE data in the context of large-scale terrestrial hydrological monitoring, such as assessing alterations in terrestrial water storage, involve: retrieving the hydrological components of GW, analysing droughts, floods, land subsidence and determining how glaciers are responding to climate change, have recently been described. India has the tenth position globally in the utilization of GRACE data. Therefore, more investigation is required to completely understand the potential of GRACE data. It was found through a review of the literature that several hydrological models have not yet been thoroughly examined with GRACE data. Furthermore, small river basins can be analysed at a fine scale with downscale GRACE data using machine learning/artificial intelligence. In the Indian context, no research has been conducted to estimate river discharge by using GRACE data.

Global patterns and drivers of tropical aboveground carbon changes

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Abstract

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr−1 throughout the global tropics from 2010 to 2020. This arises from a gross loss of −1.79 PgC yr−1 offset by a marked gain of 2.01 ± 0.06 PgC yr−1. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Modeling and Statistical Approaches for Air Pollution Analysis

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Abstract

This chapter thoroughly explores modeling and statistical approaches for addressing the persistent global challenge of air pollution, emphasizing its significant impact on public health and environmental sustainability. From a historical perspective, the study traces the evolution from localized industrial impacts to current transboundary and global considerations, with examples from Africa highlighting the historical context and environmental awareness. Modeling and statistical methodologies, including dispersion models, time series analysis, source apportionment techniques, and machine learning applications, and this chapter highlights their historical significance as well as current and future relevance for air pollution mitigation. Furthermore, contemporary modeling approaches, categorized into atmospheric dispersion modeling, chemical transport models, and hybrid models, provide unique insights into pollutant behavior. Statistical techniques involving time series analysis, source apportionment, and machine learning applications showcase their adaptability in diverse air pollution contexts. Case studies involving urban air quality modeling, industrial emissions, and regional/global perspectives, highlighting challenges and effective mitigation strategies. The analysis of challenges and limitations emphasizes issues such as data quality, model validation, uncertainty, and computational complexity, crucial for refining methodologies. Future directions outline emerging technologies, remote sensing, IoT integration, and the role of air pollution analysis in policy formulation, signaling a new era of proactive air quality management informed by cutting-edge technologies. By addressing challenges and embracing emerging technologies, the scientific community can contribute to effective air quality management and sustainable environmental practices.