Variations in trends of temperature and its influence on tree growth in the Tuscan Apennines

Gentilucci, Matteo; D'Aprile, Fabrizio

doi:10.1007/s12517-021-07546-w

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Climate change is a consolidated reality, largely due to anthropogenic emissions into the atmosphere, which affects not only hydrology but many other fields of research, such as agriculture [20,21], biodiversity [22,23], slope stability [24,25], the vegetation [26,27] and the economy in general [28]. Climate change can affect streamflow both through rising temperatures [29,30] which inevitably affect the increase in evapotranspiration and through changes in the rainfall regime [31]. Significant changes in mean temperatures and consequently in potential evapotranspiration have been shown worldwide [32], and this study area is no exception.…”

Section: Introduction 1state Of the Artmentioning

confidence: 99%

Trend Analysis of Streamflows in Relation to Precipitation: A Case Study in Central Italy

Gentilucci

Djouohou

Barbieri

et al. 2023

Water

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The monitoring of water resources is becoming increasingly important for humid temperate climates in light of climate change, which shows a generalised increase in temperatures and a decrease in precipitation, which is not generalised but relative to the area of interest. In this context, it is interesting to understand what the climatic changes have been, in terms of precipitation and how they have affected streamflows, by analysing them on a monthly basis. At the basin scale, interpolations were carried out with geostatistical methods using GIS software, spatialising the areal distribution of precipitation and obtaining an average value that can be correlated with water flows. As a pilot project, this research analysed the Upper Potenza basin in relation to the flow rates of the Potenza River over two reference periods, from 1964 to 1979 and from 2005 to 2020. The results show a decreasing trend in streamflows within the studied basin, while the precipitation trend decreases for the period 1964–1979 and increases for the period 2005–2020. Effective precipitation, in turn, shows a rather pronounced decrease in the more recent 2005–2020 period, due to climate change influencing the increase in temperature and consequently, the increase in evapotranspiration. In this context, it is significant to note that the Pearson correlation coefficient of streamflow to effective rainfall for both periods is about 0.8, suggesting that the net of anthropogenic disturbances, streamflow and actual precipitation maintain a high correlation. This model could be exported to other territories, in order to gain a global view for a better understanding and subsequent adaptation to ongoing climate change.

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Section: Introduction 1state Of the Artmentioning

confidence: 99%

Trend Analysis of Streamflows in Relation to Precipitation: A Case Study in Central Italy

Gentilucci

Djouohou

Barbieri

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to reports, the global temperature will increase by 2-3 • C in 2030-2050 [8], and temperature increases of up to 2 • C or higher are expected to reduce the yields of global prime crops, such as rice, maize, and wheat [9]. Furthermore, climate change has led to substantial changes in the dates of planting and harvesting, which has led to changes in the growing season due to variations and uncertainties in rainfall and temperature, thereby impacting food demand [10,11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating and Adapting Climate Change Impacts on Rice Production in Indonesia: A Case Study of the Keduang Subwatershed, Central Java

2021

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Predicting the effect of climate change on rice yield is crucial as global food demand rapidly increases with the human population. This study combined simulated daily weather data (MarkSim) and the CERES-Rice crop model from the Decision Support System for Agrotechnology Transfer (DSSAT) software to predict rice production for three planting seasons under four climate change scenarios (RCPs 2.6, 4.5, 6.0, and 8.5) for the years 2021 to 2050 in the Keduang subwatershed, Wonogiri Regency, Central Java, Indonesia. The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software. The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76). Our results suggest that the predicted changing rainfall patterns, rising temperature, and intensifying solar radiation under climate change can reduce the rice yield in all three growing seasons. Under RCP 8.5, the impact on rice yield in the second dry season may decrease by up to 11.77% in the 2050s. Relevant strategies associated with policies based on the results were provided for decision makers. Furthermore, to adapt the impact of climate change on rice production, a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient management should be developed for farming practices based on our results in the study area. Our study is not only the first assessment of the impact of climate change on the study site but also provides solutions under projected rice shortages that threaten regional food security.

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Prediction of Snowmelt Days Using Binary Logistic Regression in the Umbria-Marche Apennines (Central Italy)

Gentilucci¹,

Pambianchi²

2022

Water

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Snow cover in a mountain area is a physical parameter that induces quite rapid changes in the landscape, from a geomorphological point of view. In particular, snowmelt plays a crucial role in the assessment of avalanche risk, so it is essential to know the days when snowmelt is expected, in order to prepare operational alert levels. Moreover, melting of the snow cover has a direct effect on the recharge of the water table, as well as on the regulation of the vegetative cycle of mountain plants. Therefore, a study on snowmelt, its persistence on the ground, and the height of the snow cover in the Umbria-Marche Apennines in central Italy is of great interest, since this is an area that is extremely poorly sampled and analysed. This study was conducted on the basis of four mountain weather stations equipped with a recently installed sonar-based snow depth gauge, so that a relatively short period, 2010–2020, was evaluated. A trend analysis revealed non-significant decreases in snow cover height and snow persistence time, in contrast to the significant increasing trend of mean temperature, while parameters such as relative humidity and wind speed did not appear to have a dominant trend. Further analysis showed relationships between snowmelt and the climatic parameters considered, leading to the definition of a mathematical model developed using the binary logistic regression technique, and having a predictive power of 82.6% in the case of days with snowmelt on the ground. The aim of this study was to be a first step towards models aimed at preventing avalanche risk, hydrological risk, and plant species adaptation, as well as providing a more complete definition of the climate of the study area.

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Variations in trends of temperature and its influence on tree growth in the Tuscan Apennines

Cited by 3 publications

References 38 publications

Trend Analysis of Streamflows in Relation to Precipitation: A Case Study in Central Italy

Trend Analysis of Streamflows in Relation to Precipitation: A Case Study in Central Italy

Evaluating and Adapting Climate Change Impacts on Rice Production in Indonesia: A Case Study of the Keduang Subwatershed, Central Java

Prediction of Snowmelt Days Using Binary Logistic Regression in the Umbria-Marche Apennines (Central Italy)

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