Roberto DEIDDA | University of Cagliari (original) (raw)

Papers by Roberto DEIDDA

EGU General Assembly Conference Abstracts, Apr 1, 2017

EGU General Assembly Conference Abstracts, Apr 1, 2018

Among the different nature-based solutions proposed for the sustainable development of urban area... more Among the different nature-based solutions proposed for the sustainable development of urban areas, green roofs are becoming more and more popular, thanks to their multiple benefits. Indeed, these nature-based solutions reduce the pluvial flood risk during rainfall events, contribute to the thermal insulation of buildings, mitigate the urban heat island effect, and improve the air quality. The knowledge that citizens have about green roofs, the interest and willingness to pay for their installation are still poorly investigated and quantified, although this meta-information could be a valid support and guidance for policy makers and urban planners. In this work, we investigated, through an anonymous online survey, the perception of people living in Sardinia on the most common urban environmental issues (i.e., urban flood, increase of temperature, energy consumption, air pollution and lack of green spaces), and the willingness to pay for green roof installation on both public and private roofs. We estimated the empirical relation among environmental issues awareness and the willingness to pay for a specific green solution while trying to relate the latter to socio demographic characteristics. Results show that citizens are very interested in having green roofs on public building, and on average they are willing to pay around 35 euro per year for their installation and maintenance. The interest for green roofs on private building is, on the other hand, lower than on public ones, due to the high installation and maintenance costs. Moreover, when possible, citizens would rather have solar panels instead of green roofs, since they fully perceive the economic advantages deriving from the installation and are not fully aware of the green roof benefits.

AGU Fall Meeting Abstracts, Dec 1, 2019

Journal of Environmental Management, Oct 1, 2023

Journal Of Geophysical Research: Atmospheres, Feb 8, 2018

The evaluation of regional climate models' (RCMs) ability to reproduce the presentday climate is ... more The evaluation of regional climate models' (RCMs) ability to reproduce the presentday climate is critical to support their utility in impact studies under future climatic scenarios. This study evaluates the skill of an ensemble of state-of-the-art regional climate simulations from the EURO-CORDEX initiative in reproducing the precipitation (P) properties in Sardinia, a Mediterranean island of ~24,000 km 2. The ensemble includes simulations at 0.44º and 0.11º grid spacing of the "Historical" experiment from 1950 to 2005. Precipitation records from a high-density network of gauges are used as reference dataset. The interannual and seasonal climatology of P, presence of trend, and orographic effect are assessed at the original RCM grid spacings and different spatial scales of aggregation. Most models capture the observed positive relation between annual P and elevation, with better performance at 0.11º spacing. The simulated spatial patterns of the climatological annual and seasonal means are well correlated with the observation at both spacings, but their spatial variability is overestimated. Positive and negative bias of up to ±60% are found in the simulation of annual mean and interannual variability. While the majority of the models reproduce the phase of the seasonal cycle, they underestimate (overestimate) winter (summer) P. The RCMs exhibit different deficiencies in capturing the negative annual and seasonal observed trends. In general, models' skill degrades when analyses are conducted at smaller aggregation scales. Results of this study reveal insight on RCM performances in small-scale regions, which are often targeted by impact studies and have so far received less attention.

<p>According to both theoretical considerations and climatic projections, precipita... more <p>According to both theoretical considerations and climatic projections, precipitation extremes are expected to increase under a warmer environment. Inferential analyses involving statistical testing procedures are frequently performed to validate this scenario. Recent research has found that the results of trend tests applied to hydrological data might be misinterpreted if (i) records exhibit autocorrelation and (ii) field significance is not taken into account when tests are performed multiple times. In this study, we investigate these two issues focusing on frequencies (or counts) of daily rainfall extremes. To this end, a sample of extreme precipitation frequency time series is derived from long-term (100-year) daily precipitation records retrieved by 1087 rain gauges belonging to the Global Historical Climate Network database. Several Monte Carlo simulations are performed involving random synthetic frequency time series generated through the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)), reproducing the statistical properties of the observed counts and characterized by different sample size, autocorrelation level, and trend magnitude. The following are the key findings. (1) While empirical autocorrelations are likely due to the existence of trends, empirical trends cannot be explained solely by autocorrelation, suggesting that accounting for serial correlation may have a limited influence on trend analyses of extreme frequency time series. (2) Taking field significance into account enhances the interpretation of test results by reducing the type-I errors. (3) Parametric statistical trend tests based on linear and Poisson regression prove more powerful than non-parametric tests (e.g. Mann-Kendall test) in analyzing count series. (4) Finally, we use these insights to conduct trend assessments on observed counts, finding several clear spatial patterns of statistically significant increasing (decreasing) trends, mostly located in central and eastern United States and Northern Eurasia (southwestern United States, southern Europe, southern parts of Australia).</p>

EGU General Assembly Conference Abstracts, Apr 1, 2012

The study of rainfall statistical variability is important for a wide range of water-related disc... more The study of rainfall statistical variability is important for a wide range of water-related disciplines. Rainfall intermittency is often referred to two different aspects of variability: (i) the sudden variations of intensity, and (ii) the alternation of dry and wet periods, which can be analyzed through the construction of the binary series (BS). In this study, we characterize rainfall intermittency in time using a dataset collected by more than 200 tipping-bucket rain gages, covering the entire territory of Sardinia, Italy. For each gage, we sampled the rainfall signal at a resolution of 1 min and selected time sequences of ∼45 days, thus focusing on a range of scales interesting for hydrological applications. On each sequence, we applied several techniques to investigate intermittency, including spectral and scale invariance analysis, and computation of clustering and intermittency exponents. The spectral analysis reveals the existence of two scaling regimes, typical of stratiform (from 3 days to 2.5 h) and convective (from 2.5 h to 2 min) systems, consistent with other studies. Investigation of scale invariance for higher moments shows the existence of an additional breaking point at 15-30 min. The change of statistical properties of the rainfall signals at these scales is also confirmed by the clustering and intermittency exponents. These metrics indicate that (i) the BS shows a behavior similar to the white noise at scales lower than 15-30 min, and (ii) rainfall intensity fluctuations tend to attenuate (amplify) the intermittency of the BS at scales smaller (larger) than 15-30 min. Finally, thanks to the availability of a large dataset, we show the presence of spatial patterns for the metrics characterizing rainfall intermittency, which can be explained by the geographical location and the topographic features of the gages, and by the interaction of these characteristics with the dominant weather conditions.

A regional frequency analysis of intense rainfall in Sardinia is presented. The TCEV probabilisti... more A regional frequency analysis of intense rainfall in Sardinia is presented. The TCEV probabilistic model is assumed as parent distribution of the observed extreme rainfall events. The comparisons with other two-parameter distributions point out the better capability of the TCEV model in reproducing the observed statistics. The regionalized TCEV model considers three sub-zones in the island with "similar" characteristics regarding the probabilistic annual maximum daily rainfall model. These sub-zones are then confirmed considering rainfall duration ranging from 15 min to 24 hours. In each subregion the explicit equations for depth-duration-frequency curves are then obtained considering the index rainfall. To evaluate the daily index rainfall in ungauged sites, the results obtained using three transposition techniques are compared with the kriging method giving best results. The index rainfall for shorter duration is obtained by a monomial equation whose parameters can be evaluated on the basis of daily index rainfall in the same site.

Hydrology and Earth System Sciences, Jan 29, 2013

<p>A number of studies have shown that the ability of statistical tests to ... more <p>A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.</p>

Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution fu... more Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

Journal of Environmental Management, Oct 1, 2023

The EGU General Assembly, 2005

EGU General Assembly Conference Abstracts, Apr 1, 2017

EGU General Assembly Conference Abstracts, Apr 1, 2018

Among the different nature-based solutions proposed for the sustainable development of urban area... more Among the different nature-based solutions proposed for the sustainable development of urban areas, green roofs are becoming more and more popular, thanks to their multiple benefits. Indeed, these nature-based solutions reduce the pluvial flood risk during rainfall events, contribute to the thermal insulation of buildings, mitigate the urban heat island effect, and improve the air quality. The knowledge that citizens have about green roofs, the interest and willingness to pay for their installation are still poorly investigated and quantified, although this meta-information could be a valid support and guidance for policy makers and urban planners. In this work, we investigated, through an anonymous online survey, the perception of people living in Sardinia on the most common urban environmental issues (i.e., urban flood, increase of temperature, energy consumption, air pollution and lack of green spaces), and the willingness to pay for green roof installation on both public and private roofs. We estimated the empirical relation among environmental issues awareness and the willingness to pay for a specific green solution while trying to relate the latter to socio demographic characteristics. Results show that citizens are very interested in having green roofs on public building, and on average they are willing to pay around 35 euro per year for their installation and maintenance. The interest for green roofs on private building is, on the other hand, lower than on public ones, due to the high installation and maintenance costs. Moreover, when possible, citizens would rather have solar panels instead of green roofs, since they fully perceive the economic advantages deriving from the installation and are not fully aware of the green roof benefits.

AGU Fall Meeting Abstracts, Dec 1, 2019

Journal of Environmental Management, Oct 1, 2023

Journal Of Geophysical Research: Atmospheres, Feb 8, 2018

The evaluation of regional climate models' (RCMs) ability to reproduce the presentday climate is ... more The evaluation of regional climate models' (RCMs) ability to reproduce the presentday climate is critical to support their utility in impact studies under future climatic scenarios. This study evaluates the skill of an ensemble of state-of-the-art regional climate simulations from the EURO-CORDEX initiative in reproducing the precipitation (P) properties in Sardinia, a Mediterranean island of ~24,000 km 2. The ensemble includes simulations at 0.44º and 0.11º grid spacing of the "Historical" experiment from 1950 to 2005. Precipitation records from a high-density network of gauges are used as reference dataset. The interannual and seasonal climatology of P, presence of trend, and orographic effect are assessed at the original RCM grid spacings and different spatial scales of aggregation. Most models capture the observed positive relation between annual P and elevation, with better performance at 0.11º spacing. The simulated spatial patterns of the climatological annual and seasonal means are well correlated with the observation at both spacings, but their spatial variability is overestimated. Positive and negative bias of up to ±60% are found in the simulation of annual mean and interannual variability. While the majority of the models reproduce the phase of the seasonal cycle, they underestimate (overestimate) winter (summer) P. The RCMs exhibit different deficiencies in capturing the negative annual and seasonal observed trends. In general, models' skill degrades when analyses are conducted at smaller aggregation scales. Results of this study reveal insight on RCM performances in small-scale regions, which are often targeted by impact studies and have so far received less attention.

<p>According to both theoretical considerations and climatic projections, precipita... more <p>According to both theoretical considerations and climatic projections, precipitation extremes are expected to increase under a warmer environment. Inferential analyses involving statistical testing procedures are frequently performed to validate this scenario. Recent research has found that the results of trend tests applied to hydrological data might be misinterpreted if (i) records exhibit autocorrelation and (ii) field significance is not taken into account when tests are performed multiple times. In this study, we investigate these two issues focusing on frequencies (or counts) of daily rainfall extremes. To this end, a sample of extreme precipitation frequency time series is derived from long-term (100-year) daily precipitation records retrieved by 1087 rain gauges belonging to the Global Historical Climate Network database. Several Monte Carlo simulations are performed involving random synthetic frequency time series generated through the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)), reproducing the statistical properties of the observed counts and characterized by different sample size, autocorrelation level, and trend magnitude. The following are the key findings. (1) While empirical autocorrelations are likely due to the existence of trends, empirical trends cannot be explained solely by autocorrelation, suggesting that accounting for serial correlation may have a limited influence on trend analyses of extreme frequency time series. (2) Taking field significance into account enhances the interpretation of test results by reducing the type-I errors. (3) Parametric statistical trend tests based on linear and Poisson regression prove more powerful than non-parametric tests (e.g. Mann-Kendall test) in analyzing count series. (4) Finally, we use these insights to conduct trend assessments on observed counts, finding several clear spatial patterns of statistically significant increasing (decreasing) trends, mostly located in central and eastern United States and Northern Eurasia (southwestern United States, southern Europe, southern parts of Australia).</p>

EGU General Assembly Conference Abstracts, Apr 1, 2012

The study of rainfall statistical variability is important for a wide range of water-related disc... more The study of rainfall statistical variability is important for a wide range of water-related disciplines. Rainfall intermittency is often referred to two different aspects of variability: (i) the sudden variations of intensity, and (ii) the alternation of dry and wet periods, which can be analyzed through the construction of the binary series (BS). In this study, we characterize rainfall intermittency in time using a dataset collected by more than 200 tipping-bucket rain gages, covering the entire territory of Sardinia, Italy. For each gage, we sampled the rainfall signal at a resolution of 1 min and selected time sequences of ∼45 days, thus focusing on a range of scales interesting for hydrological applications. On each sequence, we applied several techniques to investigate intermittency, including spectral and scale invariance analysis, and computation of clustering and intermittency exponents. The spectral analysis reveals the existence of two scaling regimes, typical of stratiform (from 3 days to 2.5 h) and convective (from 2.5 h to 2 min) systems, consistent with other studies. Investigation of scale invariance for higher moments shows the existence of an additional breaking point at 15-30 min. The change of statistical properties of the rainfall signals at these scales is also confirmed by the clustering and intermittency exponents. These metrics indicate that (i) the BS shows a behavior similar to the white noise at scales lower than 15-30 min, and (ii) rainfall intensity fluctuations tend to attenuate (amplify) the intermittency of the BS at scales smaller (larger) than 15-30 min. Finally, thanks to the availability of a large dataset, we show the presence of spatial patterns for the metrics characterizing rainfall intermittency, which can be explained by the geographical location and the topographic features of the gages, and by the interaction of these characteristics with the dominant weather conditions.

A regional frequency analysis of intense rainfall in Sardinia is presented. The TCEV probabilisti... more A regional frequency analysis of intense rainfall in Sardinia is presented. The TCEV probabilistic model is assumed as parent distribution of the observed extreme rainfall events. The comparisons with other two-parameter distributions point out the better capability of the TCEV model in reproducing the observed statistics. The regionalized TCEV model considers three sub-zones in the island with "similar" characteristics regarding the probabilistic annual maximum daily rainfall model. These sub-zones are then confirmed considering rainfall duration ranging from 15 min to 24 hours. In each subregion the explicit equations for depth-duration-frequency curves are then obtained considering the index rainfall. To evaluate the daily index rainfall in ungauged sites, the results obtained using three transposition techniques are compared with the kriging method giving best results. The index rainfall for shorter duration is obtained by a monomial equation whose parameters can be evaluated on the basis of daily index rainfall in the same site.

Hydrology and Earth System Sciences, Jan 29, 2013

<p>A number of studies have shown that the ability of statistical tests to ... more <p>A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.</p>

Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution fu... more Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

Journal of Environmental Management, Oct 1, 2023

The EGU General Assembly, 2005