Nazzareno Diodato was born in Benevento, Italy in 1966 and was educated at Naples “Federico II”, where he received five years of education, culminating in a Bachelor Degree in Environmental/Territorial Planning Geospatial study in 1996. Actually Mr Diodato is a Senior GeoScientist at the Monte Pino Met European Research Observatory (MetEROBS–GEWEX network of the World Climate Research Programme). He is the unique Italian scientist who has been elected to the Royal Meteorological Society and among the youngest to have received that honor. His technical innovations in the field of Geographical Information Science have led to develop new hazard models in this research area and in that more specifically of the environmental hydrology.
Contributing Scientist to:
1) Scientific and COmputing Data Analysis (SCODA–Group) - RCOST of the University of Sannio. 2) Mediterranean Climate Variability (MedCLIVAR) - International Programme. 3) Mountain Research Initiative (MRI) - Swiss National Science Foundation. 4) International Society for Agricultural Meteorology (INSAM). 5) Commission on Ecosystem Management (CEM) - IUCN – The World Conservation Union. 6) A Long-Term Biodiversity, Ecosystem and Awareness Research Network (ALTER-Net).
Abstract: This work presents methods where monthly based climate data are used to estimate reference evapotranspiration (ET 0). The objective was to evaluate two monthly ET 0 models (Hargreaves-Samani, HS; Droogers-Allen HS, DAHS) and compare the results with an improved model (reference evapotranspiration model for complex terrains, REMCT). HS and DAHS are both based on the monthly temperature range (ÎT), while REMCT replaces ÎT with a monthly adjusted function (reference minimum air temperature). The test area was peninsular-insular Italy, where 13 stations with sufficient data to calculate FAO-56 Penman-Monteith ET 0 were available. The three models were evaluated against FAO-56 over a validation data set of six stations, using a multiple-statistics indicator: 0 (best) ⤠IET ⤠1 (worst). The REMCT estimates generally compared well with the FAO-56 estimates (mean IET = 0.029 against 0.187 and 0.255 with HS and DAHS, respectively). The three models performed similarly at low-altitude sites. REMCT was superior at sites higher than 500 m above sea level, where the ET 0 â ÎT relationship was distorted (e.g., by an asymmetric lapse rate between maximum and minimum air temperatures).
Abstract: This work analyses the climatic information of 607 weather anomalies belonging to a large documentary sources heritage of the continental southern Italy during the period 1675â1868. The collected information, mainly originating in Samnium River Region (SRR), were codified to obtain quantitative indices representative of a preliminary reconstruction of the precipitation anomalies. Historical written records of weather conditions that affect agriculture and living conditions have been taken as a proxy for instrumental observations of the relative wetness and dryness. As a consequence a numerical index was established to characterize the rainfall regime and its evolution. So, for the first time a series of the precipitation anomalies in SRRâcontinental southern Italy during the second half of the Little Ice Age was generated, and subsequently jointed to the instrumental series (1869â2002). Afterwards, in order to identify possible climatic change situations from 1675 today Normalized Cumulative Anomalies (NCA)âserie's and Climograms were produced. This historical period offered a sufficient range of natural variability in climate and circulation together with their relationships. Wettest period were detected in the 19th century, while that driest in the 18th century. However, the Mediterranean climate appearing from our study is far more complex than can be captured by a simple classification. In this way, the final picture is one switching between significantly different climate modes becoming apparent on several space-time-scales during the Late Little Ice Age.
Abstract: This work presents three empirical models (MMFI, Morais Modification of Fournier Index; GJRM, GrimmâJonesâRuscoâMontanarella; REMDB, DiodatoâBellocchi RainfallâErosivity Model) where monthly-based climate data are used to estimate long-term (R)USLE (Universal Soil Loss Equation and its Revisions) rainfall erosivity factor (Rm, MJ mm hâ1 haâ1 monthâ1). The objective was to evaluate two known models (MMFI and GJRM), and compare the results with the novel model REMDB meant for complex terrains. MMFI and GJRM are both based on the precipitation amount, whilst REMDB takes site latitude, elevation and precipitation seasonality also into account. The test area was the Italian region, where 30 stations (altitudes from about sea level up to 1270 m, over the latitudinal range 36â46°North) with sufficient data to calculate Rm according to USLE were available. The three models were evaluated against USLE rainfall erosivity over a validation data set of 14 stations, using a range of performance statistics. The REMDB estimates generally compared well with the USLE estimates according to different statistics. For REMDB, the relative root mean square error was, in average, 48.58% against 71.49% for MMFI and 66.55% for GJRM. The average modelling efficiency of REMDB was 0.51 against â0.02 (MMFI) and 0.13 (GJRM). REMDB was also superior in preventing biased errors in time, as quantified by the average pattern index versus months: 17.65 MJ mm hâ1 haâ1 monthâ1, against 58.54 MJ mm hâ1 haâ1 monthâ1 (MMFI) and 57.76 MJ mm hâ1 haâ1 monthâ1 (GJRM). Of the two simplified models, the MMFI was the worst performer while the GJRM model performed similarly to the REMDB at two mid-altitude sites of Central Italy.
Abstract: Meteorological stations placed in remote areas (such as mountainous sites) often record only precipitation and air temperature data, summarised into monthly tables. There is therefore a need for appropriate methods to estimate solar radiation data from monthly inputs. It is important to know the quality and characteristics of the estimates made in order to understand what impacts the modelled data may have on the use to which they are meant. This paper evaluates the behaviour of two empirical models based on the difference between maximum and minimum air temperatures and compares results with a newly developed model, for 24 stations (11 for model calibration, the rest for model validation) in Italian sites. The new model (SRMCT, solar radiation model for complex terrains) replaces the minimum air temperature with a monthly function and uses rainy days number to correct the air temperature range. Comparisons were made using a fuzzy-logic-based multiple-statistics assessment approach (Irad). An appraisal of the temporal distribution of mean errors over a year was also performed. The new model produced the best overall estimates. The two air temperature-based models can be an alternative approach when only air temperature data are available, but at many sites they introduced large inaccuracies and seasonal patterns in the error distribution. The study demonstrates the appropriateness of both air temperature range and rain correction factors for monthly based solar radiation estimates. At the same time, it shows the value and importance of using a range of assessment statistics, even aggregated into an overall indicator, to evaluate model estimates.
Abstract: The growing concern of the possible impact of natural disasters and extreme events on the environment has recently created new demands for information from, and assessment by, environmental engineers and climatologists. Mediterranean river basins encompass a diverse range of valley and floodplain environments and are affected by many different processes, such as hydroclimate, land-use, catchment geology, and relief. The objective of this paper is to develop a methodology for exploratory rainstorms data analysis and uncertainty estimation in regional hydrogeomorphological frequency analysis (HFA). A general framework is proposed for designing spatial variability of rainstorms with assigned return periods (T) inducing multiple damaging hydrogeomorphological events in the Campania region, south Italy. To this end, the analysis of precipitation, involving means techniques of regional HFA and geostatistical theory integrated approaches, is a subject of great interest. However, the hydrogeomorphological consequences depend on complex interactions between extreme precipitation and torrential characteristics of the landscape. In Mediterranean environments, climatic fluctuations in hydrological regime, especially those exceeding rain thresholds for an acceptable range of flexibility, can be the main causes of relevant hydrogeomorphological impacts. An algorithm for the characterization of this impact termed with the acronym DRHI (Design Rainstorms Hazard Index) in this paper is presented first. Next, the continuous DRHI data are converted at each location using a binary variable indicator transform based on critical thresholds. In the third step, the expansion of DRHI-soft information from point to landscapes were assessed geostatistically using the records of 70 rain stations of the Department of Civil Protection established by Regional Monitoring Networks. In this way, threshold values for extreme hydrogeomorphological impacts were selected based on geographical distribution patterns of precipitation maxima with a duration of 3 h for a return period of 10, 20, and 50 years. Moreover, the map-based method provides the identification where future infill sampling should be focused in support of more precise characterization.
Abstract: Land use change has been recognized throughout the Earth as one of the most important factors influencing the occurrence of rainfall-driven geomorphological processes. However, relating the occurrence of historical soil erosion rates is difficult because of the lack of long-term research projects in river basins. Also, complex models are not adequate to reconstruct erosion rate changes because they require significant input data not always available on long timescales. Given the problems with assessing sediment yield using complex erosion models, the objective of this study is to explore a parsimonious scale-adapted erosion model (ADT) from the original Thornes and Douglas algorithms, which aims at reconstruction of annual net erosion (ANE) upon multisecular timescales. As a test site, the Calore River basin (3015 km2 in southern Italy) provides a peculiar and unique opportunity for modelling erosion responses to climate and land cover changes, where input-data generation and interpretation results were also supported by documented hydrogeomorphological events that occurred before and after land deforestation. In this way, ANEADT-values were reconstructed for the period 1675â2004 by using precipitation indexes, complemented by recent instrumental records, and by using land cover statistics from documented agrarian sources. Pulses of natural sedimentation in the predeforestation period have been related to Vesuvius volcanic activity and changes in rainstorm frequency. After deforestation, the basin system became unstable with sudden fluctuations in the hydrogeomorphological regime contributing significantly to increased erosion and, in turn, sediment transport sequences via river drainage towards the Tyrrhenian coast.
Abstract: In this paper, a computational environinformatics (environmental informatics) operation for mapping the groundwater climatological recharge in regional sub-basin is presented. It is based on a soilâwater balance (SWB) and spatial statistics integrated in a GIS environment. Mediterranean is a region with large demands for groundwater supplies. However, water catchment data are affected by large uncertainty, arising from sampling and modelling, which makes predicting groundwater recharge difficult. Geostatistic tools for GIS are able to incorporate imput data (coverages, shape files, raster, grids) in water data processing, allowing for modeling spatial patterns, prediction at unsampled locations, and assessment of the prediction uncertainty in a meaningful way that can provide a more suitable interpretation. An issue model of linear kriging, termed as lognormal kriging in form of a probability map (LKpm), is emphasized in this study because a soft description of the recharge in terms of probability is consistent to mitigate the uncertainty of the SWB estimates. The approach was applied to a test site in the Tammaro agricultural basin (South Italy) for the incorporation of change of support in water recharge downscaling modeling. So, the estimate of uncertainty at unsampled locations, via LKpm, was used to explain the probability of exceeding a value range of the water recharge samples' distribution. In this way, the probability of exceeding the median recharge (215 mm yearâ 1) is low in the southeastern portion (48%) of the basin area and high in the northwestern remaining portion (52%).
Abstract: In this paper, a novel approach for mapping leaching risk at large sub-regional scale under limited information is presented, with acronym environinformatics in ecological risk assessment. The problem consists into quantifying the exchange frequency of the plant available soil water (Narula et al. in J Geogr Inform Decision Anal 7(1)32â46, 2002), this frequency can be adopted as a measure indicating the nutrient and contaminant leaching risk for a site. Our approach is based on integrating soil water balance with spatial analysis tools. However, any decision involved in scientific risk evaluation requires the accurate quantification of the degree of uncertainty arising from sampling, modelling and interpolation errors. The non-parametric geostatistical procedure of Indicator Kriging enables to circumvent this problem by estimating the probability that the true value exceed a set of threshold values. The transformation of leaching data to a binary response variable, known as indicator, can lead to a soft description of leaching. Such soft description can mitigate the uncertainty in exchange frequency estimates of the plant available soil water. The approach was applied to a test site in Beneventan agroecosystem (South Italy) by using a long-term hydrological water balance acquired in a 40-years period. In this way, about 400 km2 (25%) of the total 2,000 km2 of the Benevento province were classified as areas sensitive to nutrient and contaminant leaching.
Abstract: This paper presents an environmental hazard assessment to account the impacts of single rainstorm variability on river-torrential landscape identified as potentially vulnerable mainly to erosional soil degradation processes. An algorithm for the characterisation of this impact, called Erosive Hazard Index (EHI), is developed with a less expensive methodology. In EHI modelling, we assume that the river-torrential system has adapted to the natural hydrological regime, and a sudden fluctuation in this regime, especially those exceeding thresholds for an acceptable range of flexibility, may have disastrous consequences for the mountain environment. The hazard analysis links key rainstorm energy variables expressed as a single-storm erosion index (EIsto), with impact thresholds identified using an intensity pattern model. Afterwards, the conditional probabilities of exceeding these thresholds are spatially assessed using non-parametric geostatistical techinques, known as indicator kriging. The approach was applied to a test site in river-torrential landscape of the Southern Italy (Benevento province) for 13 November 1997 rainstorm event.
Abstract: Precipitation variability results from atmospheric circulation and complex site-specific bio-geoclimatic characteristics; therefore, climatic variables are expected to be correlated in a scale-dependent way. This paper studies the influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. For this purpose, the mutual benefits of an integrated geographic information system (GIS) and a geostatistics approach was used for spatial precipitation interpolation from rainfall observations measured at 51 climatic stations in a mountainous region of southern Italy (Benevento province). As no single method is optimal for all regions, it is important to compare the results obtained using alternative methods applied to the same data set. Therefore, besides ordinary kriging examination, two auxiliary variables were added for ordinary co-kriging of annual and seasonal precipitation: terrain elevation data and a topographic index. Cross-validation indicated that the ordinary kriging yielded the largest prediction errors. The smallest prediction errors were produced by a multivariate geostatistical method. However, the results favour the ordinary co-kriging with inclusion of information on the topographic index. The application of co-kriging is particularly justified in areas where there are nearby stations and where landform is very complex. We conclude that ordinary co-kriging is a very flexible and robust interpolation method because it may take into account several properties (soft and hard data) of the landscape.
Abstract: Seasonal rainerosivity is important in the structure and dynamics of Mediterranean ecosystems. The present paper contributes to the quantitative assessment of RUSLE's monthly erosion index in a data-scarce Mediterranean region. Therefore, a regionalized relationship for estimating monthly erosion index (EI30-month) from only three rainfall parameters has been obtained. Knowledge of the seasonal and annual distribution of erosivity index, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a few long data sets were used in the analysis, validation with established monthly erosivity index values from other Italian locations, suggest that the model presented (r2 = 0.973) is robust. It is recommended to monthly erosivity estimates when experimental data-scarce rainfall become available.
Abstract: Land evaluation is sensitive to the effects of variability of ecologically complex phenomena. A probability map incorporating some of these phenomena is proposed to account for local uncertainty of areas affected by soil degradation in the Apennines of south Italy. To be useful, a method for assessing soil degradation should integrate several kinds of data. We present here an overview of the geostatistical approach to solving this problem: non-linear estimation. The following factors have been considered: the soil erosion by water (geomorphologic indicator), the station aridity (bioclimate indicator), and top-soil depth (pedologic indicator). We convert the continuous data values of each variable at each location using a binary variable indicator transform based on critical thresholds. The indicator transform values for individual variables are then integrated to form multiple variable indicator transform (MVIT) to evaluate the degree of soil degradation. Areas suited to soil degradation maps delineated by geographical information system (GIS), showed that the joint probabilities of meeting specific criteria indicator Kriging were influenced by the critical threshold values used to transform each individual variable and the method of integration. So, the understanding of soil vulnerability to degradation is increased to providing a way to classify degraded regions. On the basis of this information different land uses strategies could be identified to develop sustainable assessment models of soils. For example, many countries of these disadvantaged areas, should have agro-forestation programmes that increase the heterogeneity in vegetation cover contrasting hydrological properties, thus promoting a self-regulating system for runoff and erosional soil degradation control.
