Month: September 2024

Abstract

Drought, a consequence of prolonged precipitation deficiencies, is a significant hazard exacerbated by climate change. Highly susceptible to extreme climatic events, Sri Lanka faces drought as its most prominent hazard, necessitating comprehensive assessments. This study focuses on the escalating impact of hydrological drought intensified by climate change on the Maduru Oya and Kirindi Oya dry zone basins in Sri Lanka, crucial due to their vulnerability to altered hydroclimatic dynamics. Monitoring hydrological droughts in these regions is paramount for ensuring a reliable water supply for irrigation and other purposes. The research utilizes the Streamflow Drought Index for the monitoring of hydrological droughts. It considers six CMIP6 (Sixth Phase of the Coupled Model Intercomparison Project) Global Climate Models, with the CNRM-HR-1 model chosen as the preferred model. Two future Shared Socio-economic Pathway scenarios, SSP1-2.6 and SSP5-8.5, were selected to project future climatic conditions. The Random Forest algorithm was utilized to predict future streamflow in the two selected sub-basins. The hydrological drought assessment reveals the heightened vulnerability of the Padiyathalawa sub-basin in the Maduru Oya basin, with a notable rise in moderate hydrological drought occurrences under both future scenarios. Conversely, the Wellawaya sub-basin in the Kirindi Oya basin exhibits susceptibility to frequent moderate hydrological droughts, along with an 80% increase in severe drought occurrences under the SSP5-8.5. Consequently, both basins are projected to face water scarcity in the future. This underscores the importance of implementing measures to ensure a reliable water supply, given the substantial impact of climate change on watershed hydrology.

Abstract

It is widely predicted that climate change’s adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤  − 1.5), reduced daily low-intensity rainfall (Pmm < 10 mm), and a significant increase in extreme rainfall events (Pmm ≥ 25 mm). The WEAP outcomes revealed significant variations in future water availability, demand, and potential stressors across watersheds. Cropland and livestock are the main water consumers in rural wetlands, while households, cropland (at a lesser extent), and other urban uses exert significant water demands on wetlands located in urban environments. Of three test scenarios, the one presenting wetlands as water reservoirs seemed promising than those considered optimal (based on policies regulating water use) and rational (stationary inputs but with a decrease in daily allocation). These findings highlight the impact of climate change in the Ruzizi plain, emphasizing the urgency of implementing adaptive measures. This study advocates for the necessity of the IWRM approach to enhance water resilience, fostering sustainable development and wetland preservation under changing climate.

Abstract

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Abstract

Climate change has a recognized global effect on ozone concentration, yet its impact varies across regions and countries. Local studies are imperative for precisely evaluating the accurate, robust, and up-to-date relationship between climatic variables and ozone concentration at regional scale. In this work, we elucidate the spatiotemporal and seasonal variability of ground-level ozone (O₃) in Malaysia using backward trajectory and Generative Additive Model. Concentrations of O₃ and other air pollutants (NO₂, CO, SO₂ and PM_2.5) from a total of 43 air quality stations across the country from 2107 to 2020 have been analyzed along with the meteorological auxiliary data. Ozone pollution is susceptible in the Central, Northern and Southern of Peninsular Malaysia, and occurs at different times subject to the monsoon variability. In the Central zone, 60% of days during March and April had unhealthy ozone levels with a maximum daily averaged O₃ 73.5 ± 9.3 ppb. The backward trajectory analysis indicates that ozone pollution in the Central zone is strongly affected by northeasterly transboundary air pollution from Indochina and East China. The Generative Additive Model analysis highlights O₃ variability in the Central zone is possibly modulated by stratospheric air intrusion and PM_2.5 inhibitory effect that suppressed surface solar radiation and weakened O₃ production. Overall, the work advances the understanding of O₃ variability in Malaysia, provides valuable insights into complex interplay between O₃ concentrations and climatic variables, and offers a framework for future research in air quality modeling.

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.

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.

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.