Education, acculturation, and adaptation: Unaccompanied migrant and refugee youths in Italy

Migration worldwide is characterized by the presence of unaccompanied minors, separated from their parents or any primary caregivers. The present study aimed to fill literature gaps on the acculturation and adaptation of unaccompanied migrant and refugee adolescents. The Ward and Geeraert model (2016), which explores how acculturation unfolds within different ecological contexts, namely societal, institutional and familial, was employed to understand which aspects of the youths’ reality in the host country can facilitate or challenge their psychological and sociocultural adaptation. Since education is vital for the psychosocial and economic adjustment of migrant adolescents, this research focused on the various learning settings of unaccompanied youths, including schools, residential communities, and initiatives offered by NGOs. Semi-structured interviews were conducted with nine adolescents and eleven stakeholders in Italy, one of the European countries hosting the highest number of unaccompanied minors. Language barriers and perceived discrimination were described as potentially challenging the youths’ psychological adaptation, further complicated by their transition to adulthood. Normative developmental changes seemed to add weight to acculturative stressors and the termination of the protection system at eighteen meant that these adolescents had to abandon academic and career ambitions to become self-sufficient. Educational settings provided youths with competence in both majority and ethnic cultures, thus potentially favouring their sociocultural adaptation. However, the difficult access to mainstream Italian schools deprived them of a significant channel to integration, which was promoted by alternative measures that could be internationally employed, such as the “Apartments for Autonomy” and voluntary guardianship

Understanding a high resolution regional climate model's ability in simulating tropical East Africa climate variability and change

Includes bibliographical referencesThe main aim of this thesis is to investigate the potential benefits of increasing resolution in regional climate models in the simulation of climate variability and change over East Africa. This study is based on two high resolution regional climate simulations with a horizontal resolution of 50km and 10km, respectively. These represent present day climate and a projection of future climate change over East Africa. The regional climate model (RCM) used here is HIRHAM5, which is driven by the global circulation model (ECHAM5). Downscaled ECHAM5 output is used to drive the 50km HIRHAM5 simulation for the period 1950-2100, and output from this simulation is used to drive the 10km simulation for three time slices: 1980-1999, representative for present-day climate and two time slices for near future (2046-2065) and far future (2080- 2099), respectively. HIRHAM5 is evaluated with respect to the observed mean climatologies of rainfall, surface temperature and surface winds over East Africa, and representations of the observed annual cycles and inter-annual variability of rainfall and surface temperature. This study utilizes reanalysis and observational datasets: a hindcast of HIRHAM5 forced with ERA Interim, as well as two observation datasets for temperature and rainfall. Since reanalyses aim to make "best use" of all available observations by making a physically consistent representation continuous in time and space, and since there is a paucity of observations over many parts of Africa, the ERAI reanalysis is also used as a best estimate for model evaluation. Additionally, for evaluation of the bimodal nature of East Africa's rainfall, especially over Tanzania, three stations run by the Tanzania Meteorological Agency were used. The model data used in th is evaluation ranges from 1980 to 2006 iv HIRHAM5 demonstrates reasonable skill in the reproduction of observed patterns of mean climatology of rainfall, surface temperature and winds over East Africa. Moreover, the patterns of annual cycles of rainfall and surface temperature in the bimodal nature of East Africa are well represented. Furthermore, the model showed reasonable skill in the representation of the inter- annual variability and ENSO signals as suggested by the observation. Despite these strengths, HIRHAM5 shows some shortcomings. One weakness of the model is the simulation of the magnitude of a given variable over a specific region. For example, HIRHAM5 driven by ERAI underestimates rainfall and overestimates surface temperature over the entire domain of East Africa. The higher resolution HIRHAM5 (10km resolution) overestimates rainfall over high ground. The model bias could be due in part to the inadequacy of the observation networks in East Africa, represented in this thesis by the CRU and FEWS datasets. However, these two datasets draw on some different sources and neither do they have the same resolution. FEWS is a high resolution data (0.1 o ) gridded satellite-derived precipitation estimate covering the entire African continent while CRU datasets is a relatively low resolution (0.5 o ) dataset based on rain gauge monthly precipitation only; in addition , near surface temperature is also available. As no reliable wind observations exist, wind data was taken from the ERA-Interim reanalysis. The different observational datasets do not agree particularly well, which impedes evaluating the quality of the HIRHAM5 simulations, in particular the high resolution one. So while the higher resolution HIRHAM5 appears to be generally reliable, caution must be exercised in formulating conclusions from the results, especially over high ground and remote areas without adequate observation data. Under these constraints, the results suggest HIRHAM5 may be useful for assessing climate variability and change over East Africa. A weakness of the analysis presented here is that only one combination of GCM and RCM could be investigated in depth due to computer and time constraints. Therefore the results presented here, if used in application for climate change adaptation, should be considered in conjunction with a broader suite of data, such from the CORDEX programme. This has potential to increase the reliability of information about climate variability and change at a regional to local level necessary for impact assessment

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

This research article was published by Scientific African volume 25 2024The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction