Lorenzo Vergni

Analysis of agricultural drought characteristics through a two-dimensional copula

The potential of a copula model for the description of the joint probability distribution of two agricultural drought characteristics, the relative onset RO and the relative severity RS, is investigated in Perugia (Central Italy) in reference to a sunflower crop. The 1924–2009 time series of daily precipitation and maximum and minimum temperature were used to simulate, by means of the AquaCrop model, the root-zone soil water dynamics (SWt) and the crop yield under rainfed conditions. The seasonal values of RO and RS were quantified, by applying the theory of runs to SWt (assumed as the drought reference variable), with a threshold equal to the crop critical point. The analysis shows that the best-fitting marginal distribution for both RO and RS is a truncated Gumbel distribution. The dependence structure of RO and RS, investigated by graphical and analytical techniques, was modeled by a Student copula, which is able to adequately reproduce both the overall and upper tail dependence among variables. Lastly, the Student copula was applied to obtain joint probabilities and bivariate return periods for RO and RS. These results, compared with the expected estimated yields, provide useful information for drought planning and management. For example, for the case study considered, it was found that the condition RO ≥ 0.43 (i.e., onset before the end of June) and RS ≥ 0.22 has a 5-year return period and is frequently associated with critical yields, and that the condition RO ≥ 0.47 (i.e., onset before mid-June) and RS ≥ 0.25 has a 10-year return period and is almost certainly associated with critical yields.

Effect of the North Atlantic Oscillation on winter daily rainfall and runoff in the Abruzzo region (Central Italy)

Abstract In this paper a composite analysis was used to assess the influence of the North Atlantic Oscillation (NAO) on the winter daily rainfall and seasonal runoff at 28 stations of the Abruzzo region (Central Italy) during the period 1951–2012. Compositing was based on NAO− and NAO+ phases, identified by mean winter values of the normalized NAO index (NAOI) ≤−0.75 and ≥+0.75, respectively. In accordance with previous studies, it was found that NAO− phases determine, in general, a greater number of wet days (Nw) and (consequently) higher seasonal rainfall amounts in comparison to NAO+ phases. However, the NAO influence is characterized by a certain spatial variability, that can mostly be explained by orographic effects due to the Apennine Mountains. This is particularly evident for the mean rainfall depth per event (Pe) that, during NAO− phases, increases for the stations to the west of the Apennines, while it decreases for most of the stations to the east. The structure of winter daily rainfall of NAO+ and NAO− type, was described by a simple but effective first-order Markov process, determining the transition probabilities P01 (dry to wet) and P10 (wet to dry) and modelling the rainfall depth on wet days by a Weibull distribution. The most significant influence of NAO concerns P01 and the shape parameter of the Weibull distribution that are both higher during the NAO− phase. This means that NAO− phases are characterized by less persistent dry periods and less variable daily rainfall depths, in comparison to NAO+ phases. The effect of these differences on the winter seasonal runoff was explored by applying a Curve Number rainfall-runoff model. Significant increments of the mean seasonal runoff during NAO− phases were observed only for few stations (mainly on the west), characterized by corresponding increments of Nw, Ptot and Pe.). NAO+ phases, instead, are characterized by relevant increments of the seasonal runoff variability, particularly on the eastern areas. In this context, the important regulating function of the watershed conditions was also discussed.

Uncertainty in drought monitoring by the Standardized Precipitation Index: the case study of the Abruzzo region (central Italy)

As shown by several authors, drought monitoring by the Standardized Precipitation Index (SPI) presents some uncertainties, mainly dependent on the choice of the probability distribution used to describe the cumulative precipitation and on the characteristics (e.g., length and variability) of the dataset. In this paper, the uncertainty related to SPI estimates has been quantified and analyzed with regards to the case study of the Abruzzo region (Central Italy), by using monthly precipitation recorded at 75 stations during the period 1951–2009. First, a set of distributions suitable to describe the cumulative precipitation at the 3-, 6-, and 12-month time scales was identified by using L-moments ratio diagrams. The goodness-of-fit was evaluated by applying the Kolmogorov–Smirnov test, and the Normality test on the derived SPI series. Then the confidence intervals of SPI have been calculated by applying a bootstrap procedure. The size of the confidence intervals has been considered as a measure of uncertainty, and its dependence on several factors such as the distribution type, the time scale, the record length, and the season has been examined. Results show that the distributions Pearson type III (PE3), Weibull (WEI), Generalized Normal (GNO), Generalized Extreme Value (GEV), and Gamma (GA2) are all suitable to describe the cumulative precipitation, with a slightly better performance of the PE3 and GNO distributions. As expected, the uncertainty increases as the record length and time scale decrease. The leading source of uncertainty is the record length while the effects due to seasonality and time scale are negligible. Two-parameter distributions make it possible to obtain confidence intervals of SPI (particularly for extreme values) narrower than those obtained by three-parameter distributions. Nevertheless, due to a poorer goodness of fit, two-parameter distributions can provide less reliable estimates of the precipitation probability. In any event, independently of the type of distribution, the SPI estimates corresponding to extreme precipitation values are always characterized by a relevant uncertainty. This is due to the explosion of the probability variability that occurs when precipitation values approach the tails of the supposed distribution.