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Journal Article•DOI•

Drought events at different timescales in southern Italy (Calabria)

Gabriele Buttafuoco1, Tommaso Caloiero1•
National Research Council1
29 May 2014-Journal of Maps (Taylor & Francis)-Vol. 10, Iss: 4, pp 529-537
TL;DR: In this paper, the authors report an analysis of the spatial drought patterns for a region of southern Italy (Calabria) based on a homogenous monthly precipitation data set of 129 rain gauges for the period 1916-2006.
Abstract: This study reports an analysis of the spatial drought patterns for a region of southern Italy (Calabria) based on a homogenous monthly precipitation data set of 129 rain gauges for the period 1916–2006. Drought was expressed using the Standardized Precipitation Index (SPI), and drought events were analyzed using both the short-time (3 and 6 months) and the long-time (12 and 24 months) SPI. In particular, in order to characterize the SPI spatial pattern, index data of the three most severe drought events were interpolated and mapped using a geostatistical approach. Results show that these heavy drought episodes have widely affected the Calabria region and the drought that occurred in 2002 was the worst in terms of spatial extent both at short- and long-time scales.

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Maps of drought events at different
timescales in southern Italy (Calabria)
Gabriele Buttafuoco and Tommaso Caloiero
Institute for Agricultural and Forest Systems in the Mediterranean
National Research Council of Italy (CNR-ISAFOM)
Via Cavour, 4-6 - 87036 Rende (CS) - Italy
gabriele.buttafuoco@cnr.it; tommaso.caloiero@isafom.cnr.it
Geographic Coordinate System: WGS84
The Map has been compiled by Buttafuoco G. and Caloiero T.
© Journal of Maps
3-MONTH 6-MONTH 24-MONTH 12-MONTH
MARCH 1990 MARCH 1992 FEBRUARY 2002
3-MONTH 6-MONTH 24-MONTH 12-MONTH
Maps scale: 1:1,500,000
Standardized Precipitation
Index (SPI) Classes
Extreme drought
Severe drought
Moderate drought
Near normal
Coastal Chain
Pollino Massif
Ionian Sea
Sila Plateau
Serre Chain
CAMPANIA
BASILICATA
SICILY
Aspromonte
Ionian Sea
Tyrrhenian Sea
Adriatic Sea
Mediterranean Sea
Italy
France
Spain
Algeria
Tunisia
Greece
Slovenia
Croatia
Bosnia
Albania
Montenegro
0 100 20050 Kilometers
20°E
15°E10°E5°E
45°N
40°N
35°N
20°E15°E10°E5°E
45°N
40°N
35°N
SPI Time Scale:
SPI Time Scale:
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
SPI Classes
Extreme drought
Severe drought
Moderate drought
Near normal
Mount Poro
(707 m a.s.l.)
Mount Dolcedorme
(2,267 m a.s.l.)
Mount Pellegrino
(1,987 m a.s.l.)
Mount Pecoraro
(1,420 m a.s.l.)
Mount Montalto
(1,955 m a.s.l.)
Mount Botte Donato
(1,928 m a.s.l.)
40°0'N
17°30'E17°0'E16°30'E16°0'E15°30'E
39°30'N
39°0'N
38°30'N
38°0'N
17°30'E17°0'E16°30'E16°0'E15°30'E
40°0'N
39°30'N
39°0'N
38°30'N
38°0'N
m a.s.l.
Rain Gauges
Coast line
0 - 300
301 - 600
601 - 900
901 - 1,200
1,201 - 1,500
> 1,500
Regional boundaries
Main Peaks
0 20 40 60 80 10010 km
Mount Gariglione
(1,765 m a.s.l.)
Tyrrhenian Sea
Citations
More filters
Geostatistics For Environmental Scientists

[...]

Marko Wagner
01 Jan 2016
TL;DR: The geostatistics for environmental scientists is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for reading geostatistics for environmental scientists. As you may know, people have search numerous times for their favorite novels like this geostatistics for environmental scientists, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their desktop computer. geostatistics for environmental scientists is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the geostatistics for environmental scientists is universally compatible with any devices to read.

508 citations

Journal Article•DOI•
Drought Analysis in Europe and in the Mediterranean Basin Using the Standardized Precipitation Index

[...]

Tommaso Caloiero, Simone Veltri, Paola Caloiero, Francesco Frustaci
07 Aug 2018-Water
TL;DR: In this paper, the authors analyzed drought events over a large area of the Northern Hemisphere, including continental Europe, Ireland, the United Kingdom, and the Mediterranean basin, using the Standardized Precipitation Index (SPI) at various times scales (3, 6, 12, and 24 months).
Abstract: In this study, drought events over a large area of the Northern Hemisphere, including continental Europe, Ireland, the United Kingdom, and the Mediterranean basin, were analyzed using the Standardized Precipitation Index (SPI) at various times scales (3, 6, 12, and 24 months). To this purpose, the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product Version 2018 data set, with spatial resolutions of 0.5° longitude/latitude and for the period 1951–2016, has been used. First, the temporal evolution of the percentage of grid points, falling within the severe and extreme drought categories, has been evaluated. Then, a trend analysis has been performed at a seasonal scale, considering the autumn-winter and the spring-summer periods, and at an annual scale. The results of this paper highlight that the Mediterranean basin and North Africa are the most consistently vulnerable areas showing a general reduction in SPI values especially for the long time scale.

75 citations

Journal Article•DOI•
Analyses of Drought Events in Calabria (Southern Italy) Using Standardized Precipitation Index

[...]

Gabriele Buttafuoco, Tommaso Caloiero, Roberto Coscarelli1•
National Research Council1
01 Jan 2015-Water Resources Management
TL;DR: In this paper, the authors analyzed the impact of a deficit in precipitation on soil moisture, snowpack, streamflow, groundwater and reservoir storage in a region of southern Italy (Calabria) using a homogenised and gap-filled database.
Abstract: A deficit in precipitation has different impacts on soil moisture, snowpack, streamflow, groundwater and reservoir storage. In this study, drought, expressed using the SPI, has been analysed in a region of southern Italy (Calabria) using a homogenised and gap-filled database for 129 monthly rainfall series in the 1916–2006 period. Both the short-term (3 and 6 months) and the long-term (12 and 24 months) SPI were estimated and, in order to identify the worst events, the percentages of the regional area falling within severe or extreme dry conditions have been evaluated. With the aim to spatially characterize the most severe drought event, the SPI data were estimated at ungauged locations and mapped using a geostatistical approach. Finally, a time series analysis of long-term SPI was performed to detect possible trends. Results showed that several heavy drought episodes have widely affected the Calabria region and, among these events, the worst one occurred between December 2001 and April 2002. The trend analysis showed a reduction in the SPI values that is a tendency towards drier conditions, although the running trend approach, carried out only for the long-term SPI, revealed that this tendency is not persistent throughout the series length, but it depends on the period examined.

73 citations

Cites background or methods from "Drought events at different timesca..."

  • ...…Loukas and Vasiliades 2004; Sönmez et al. 2005; Vicente-Serrano 2006; Livada and Assimakopoulos 2007) and also in central (Vergni and Todisco 2011) and southern Italy (Rossi and Cancelliere 2002; Bonaccorso et al. 2003; Capra and Scicolone 2012; Capra et al. 2013; Buttafuoco and Caloiero 2014)....

    [...]

  • ...A variographic analysis was carried out in order to quantify the spatial pattern of the SPI data for the selected events with the exception of the events occurred in February, whose results have been shown in Buttafuoco and Caloiero (2014)....

    [...]

Journal Article•DOI•
Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis

[...]

Anshuka Anshuka1, Floris van Ogtrop1, R. Willem Vervoort1•
University of Sydney1
01 Jun 2019-Natural Hazards
TL;DR: In this paper, the authors evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI), in addition, the most effective combination of the SPI with its respective timescale and lead time was investigated.
Abstract: Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.

54 citations

Journal Article•DOI•
Geochemical sources of vanadium in soils: Evidences in a southern Italy area

[...]

Ilaria Guagliardi1, Ilaria Guagliardi2, Domenico Cicchella3, Rosanna De Rosa1, Nicola Ricca2, Gabriele Buttafuoco2 •
University of Calabria1, National Research Council2, University of Sannio3
01 Jan 2018-Journal of Geochemical Exploration
TL;DR: In this paper, the concentrations of vanadium in a southern Italy area, in both rural and urban soils, were analyzed in order to determine different geochemical sources. And the main aim of this study was to analyze the concentrations in a rural and an urban area.

50 citations

References
More filters
The relationship of drought frequency and duration to time scales

[...]

Thomas B. McKee, Nolan J. Doesken, John Kleist
01 Jan 1993
TL;DR: The definition of drought has continually been a stumbling block for drought monitoring and analysis as mentioned in this paper, mainly related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.
Abstract: 1.0 INTRODUCTION Five practical issues become important in any analysis of drought. These include: 1) time scale, 2) probability, 3) precipitation deficit, 4) application of the definition to precipitation and to the five water supply variables, and 5) the relationship of the definition to the impacts of drought. Frequency, duration and intensity of drought all become functions that depend on the implicitly or explicitly established time scales. Our experience in providing drought information to a collection of decision makers in Colorado is that they have a need for current conditions expressed in terms of probability, water deficit, and water supply as a percent of average using recent climatic history (the last 30 to 100 years) as the basis for comparison. No single drought definition or analysis method has emerged that addresses all these issues well. Of the variety of definitions and drought monitoring methods used in the past, by far the most widely used in the United States is the Palmer Drought Index (Palmer, 1965), but its weaknesses (Alley, 1984) frequently limit its wise application. For example, time scale is not defined for the Palmer Index but does inherently exist. The definition of drought has continually been a stumbling block for drought monitoring and analysis. Wilhite and Glantz (1985) completed a thorough review of dozens of drought definitions and identified six overall categories: meteorological, climatological, atmospheric, agricultural, hydrologic and water management. Dracup et al. (1980) also reviewed definitions. All points of view seem to agree that drought is a condition of insufficient moisture caused by a deficit in precipitation over some time period. Difficulties are primarily related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.

6,514 citations

Book•
Geostatistics for natural resources evaluation

[...]

Pierre Goovaerts
01 Jan 1997
TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.
Abstract: This book provides an advanced-level introduction to geostatistics and geostatistical methodology. The discussion includes tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation. It also details the theoretical background underlying most GSLIB programs.

4,274 citations

Journal Article•DOI•
Geostatistics for Natural Resources Evaluation

[...]

Timothy C. Coburn1•
Abilene Christian University1
01 Nov 2000-Technometrics
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and estimating the effects of noise in a discrete-time model.
Abstract: 1. Exploratory Data Analysis 2. The Random Functions Model 3. Inference and Modeling 4. Local Estimation: Accounting for a Single Attribute 5. Local Estimation: Accounting for Secondary Information 6. Assessment of Local Uncertainty 7. Assessment of Spatial Uncertainty 8. Summary

3,651 citations

"Drought events at different timesca..." refers background in this paper

  • ...Interested readers should refer to textbooks such as Goovaerts (1997), Chilès and Delfiner (1999), Wackernagel (2003), Webster and Oliver (2007), among others, for a detailed presentation of the theory of random functions....

    [...]

Book•
Geostatistics for Environmental Scientists

[...]

Richard Webster, Margaret A. Oliver
12 Mar 2001
TL;DR: In this article, the Covariance and Variogram were used to model the spatial process of spatial processes and predict local estimation or prediction in the presence of trend and factorial Kriging.
Abstract: Preface 1 Introduction 2 Basic Statistics 3 Prediction and Interpolation 4 Characterizing Spatial Processes: The Covariance and Variogram 5 Modelling the Variogram 6 Reliability of the Experimental Variogram and Nested Sampling 7 Spectral Analysis 8 Local Estimation or Prediction: Kriging 9 Kriging in the Presence of Trend and Factorial Kriging 10 Cross-Correlation, Coregionalization and Cokriging 11 Disjunctive Kriging 12 Stochastic Simulation (new file) Appendix A Appendix B References Index

2,660 citations

Journal Article•DOI•
Geostatistics for Environmental Scientists

[...]

R. Allan Reese1•
Centre for Environment, Fisheries and Aquaculture Science1
01 Jun 2009-Journal of The Royal Statistical Society Series A-statistics in Society
TL;DR: In this paper, the Covariance and Variogram were used to model the spatial process of spatial processes and predict local estimation or prediction in the presence of trend and factorial Kriging.
Abstract: Preface 1 Introduction 2 Basic Statistics 3 Prediction and Interpolation 4 Characterizing Spatial Processes: The Covariance and Variogram 5 Modelling the Variogram 6 Reliability of the Experimental Variogram and Nested Sampling 7 Spectral Analysis 8 Local Estimation or Prediction: Kriging 9 Kriging in the Presence of Trend and Factorial Kriging 10 Cross-Correlation, Coregionalization and Cokriging 11 Disjunctive Kriging 12 Stochastic Simulation (new file) Appendix A Appendix B References Index

2,050 citations

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Spatial Variability of Drought: An Analysis of the SPI in Sicily

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01 Aug 2003-Water Resources Management
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The relationship of drought frequency and duration to time scales

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