Author: Latest Results

Abstract

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ET_o) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ET_o PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series.

Abstract

As a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production.

Abstract

This study aims at estimating the inherent variability of microscale boundary-layer flows and its impact on air pollutant dispersion in urban environments. For this purpose, we present a methodology combining high-fidelity large-eddy simulation (LES) and a stationary bootstrap algorithm, to estimate the internal variability of time-averaged quantities over a given analysis period thanks to sub-average samples. A detailed validation of an LES microscale air pollutant dispersion model in the framework of the Mock Urban Setting Test (MUST) field-scale experiment is performed. We show that the LES results are in overall good agreement with the experimental measurements of wind velocity and tracer concentration, especially in terms of fluctuations and peaks of concentrations. We also show that both LES estimates and the MUST experimental measurements are subject to significant internal variability, which is therefore essential to take into account in the model validation. Moreover, we demonstrate that the LES model can accurately reproduce the observed internal variability.

Abstract

In the framework of the coordinated regional modeling initiative Med-CORDEX (Coordinated Regional Climate Downscaling Experiment), we present an updated version of the regional Earth System Model ENEA-REG designed to downscale, over the Mediterranean basin, the models used in the Coupled Model Intercomparison Project phase 6 (CMIP6). The regional ESM includes coupled atmosphere (WRF), ocean (MITgcm), land (Noah-MP, embedded within WRF), and river (HD) components with spatial resolution of 12 km for the atmosphere, 1/12° for the ocean and 0.5° for the river rooting model. For the present climate, we performed a hindcast (i.e. reanalysis-driven) and a historical simulation (GCM-driven) over the 1980–2014 temporal period. The evaluation shows that the regional ESM reliably reproduces the mean state, spatial and temporal variability of the relevant atmospheric and ocean variables. In addition, we analyze the future evolution (2015–2100) of the Euro-Mediterranean climate under three different scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), focusing on several relevant essential climate variables and climate indicators for impacts. Among others, results highlight how, for the scenarios SSP2-4.5 and SSP5-8.5, the intensity, frequency and duration of marine heat waves continue to increase until the end of the century and anomalies of up to 2 °C, which are considered extreme at the beginning of this century, will be so frequent to become the norm in less than a hundred years under the SSP5-8.5 scenario. Overall, our results demonstrate the improvement due to the high-resolution air–sea coupling for the representation of high impact events, such as marine heat waves, and sea-level height.

Abstract

This research paper focuses on implementing two Nature-Based Solutions (NBS) in the Sarantapotamos river basin upstream of Magoula settlement, evaluating their effectiveness through flood hydrograph calculations before and after NBS, and under future climate scenarios, encompassing lower, mean, and upper conditions representing ± 95%. The study area covers an area of 226 km² in Attica, Greece, susceptible to extreme flood events. The research contributes to NBS knowledge, emphasizing flood resilience and protecting settlements downstream. Land cover change and retention ponds, applied individually and combined, serve as NBS approaches. Flood hydrographs are calculated using the time–area (TA) diagram method in a geographic information system (GIS) with the Hydrological Engineering Center’s Hydrological Modeling System (HEC-HMS). Results demonstrate NBS effectiveness in current climate conditions, reducing peak discharge by 9.3% and 28% for land cover change and retention ponds, respectively. The combined NBS achieves a 40.5% peak discharge reduction and a significant 15.7% total flood volume decrease. Under climate change scenarios, impacts on design precipitation and flood hydrographs vary. The upper climate change scenario exhibits a 3348% increase in peak discharge and a 600% rise in total flood volume, while the lower scenario sees a 44.6% reduction in total flood volume. In the mean climate change scenario, land cover change and retention ponds reduce peak discharge by 9.73% and 23.11% and total flood volume by 9.25% and 2.17%, respectively. In conclusion, retention ponds show substantial peak discharge reduction, while land cover changes extend the time to peak, emphasizing their potential in flood risk management.

Abstract

The tails of the probability distribution host extremes. The distributions are typically classified into heavy or light-tailed distributions subjected to their tail behavior, out of which the former signifies frequent happenings of extreme events. The present study demonstrates the analysis where the outputs from 13 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used to evaluate changes in the tail behavior of precipitation extremes that will preside over India for the twenty-first century. A straightforward empirical index known as the “obesity index” (OB) is utilized to measure the tail heaviness for each of the 4801 daily precipitation records over India for historical (1970–2019) and future (2020–2100) time periods. The same approach was used to characterize daily precipitation tails in the Indian meteorological subdivisions and across different climate types during various periods. The results highlight that heavy-tailed distributions are well-suited for daily precipitation extremes in India, with OB values above 0.75 observed in nearly all grids for both present and future scenarios. Notably, in the case of the shared socioeconomic pathway (SSP) 585 climate scenario, which is the worst climate scenario, approximately 42.82% of grids exhibit the highest range of OB from 0.85 to 0.9 relative to other SSP scenarios. The findings also show that the largest to smallest heavy tails are associated with major climate types E (polar), B (arid), A (tropical), and C (temperate). Large heavy-tailed extremes are observed in ET, BSh, BWh, and Aw for climate subtypes, while relatively lighter-tailed extremes were observed in Am and Cwb. Furthermore, the variation in the OB is found to be non-linear with the elevation. In climatic zones Aw, BSh, Cwa, and ET, a U-shaped pattern is observed, while in climate zone Cwb, it shows a concave increase. Conversely, curves are convex decreasing for As, BWh, Csa, and convex increasing for zone Am. The conclusions from this study can help policymakers in designing adaptation plans in response to the anticipated effects of climate change.

Abstract

Rainfall is changing in intensity and abundance for much of the world as a result of global climate change. Rwanda has been negatively affected by a changing climate, exacerbated by human impact on land and water resources. In most parts of the country, the rainfall pattern has changed over the last decades resulting in both enhanced flooding and water shortage/scarcity in much of the country, especially in the Capital City of Kigali and peripheries which is the main economic hub of the country with strong links to the East African region. Changes in precipitation have affected agricultural production, hydropower production, and water supplies, and has been a result of increased flash floods in the city. This study developed a new predictive model of rainfall patterns in the City of Kigali (CoK) in the Republic of Rwanda using evolutionary methodologies that apply machine learning techniques of Fuzzy Inference Systems (FIS) trained via Genetic Algorithms, Neuro Network Systems and a comparative Support Vector Machine tool, and assessment downscaled climate change combinations with predicted rainfall patterns. The models were calibrated and validated using measured rainfall data in the City of Kigali from 1991 through 2023. The model results show the developed Geno Fuzzy Inference System (GENOFIS) model performed better than the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) models. The Coefficient of Efficiency (CE), and Root Mean Square Error (RMSE) were used as diagnostic measures for model performance evaluation. Models generated with GENOFIS are therefore recommended for rainfall and related prediction patterns in the City of Kigali for climate change adaptation and resilience policy and planning.