Barbara Tomassetti

Changing hydrological conditions in the Po basin under global warming.

The Po River is a crucial resource for the Italian economy, since 40% of the gross domestic product comes from this area. It is thus crucial to quantify the impact of climate change on this water resource in order to plan for future water use. In this paper a mini ensemble of 8 hydrological simulations is completed from 1960 to 2050 under the A1B emission scenario, by using the output of two regional climate models as input (REMO and RegCM) at two different resolutions (25 km-10 km and 25 km-3 km). The river discharge at the outlet point of the basin shows a change in the spring peak of the annual cycle, with a one month shift from May to April. This shift is entirely due to the change in snowmelt timing which drives most of the discharge during this period. Two other important changes are an increase of discharge in the wintertime and a decrease in the fall from September to November. The uncertainty associated with the winter change is larger compared to that in the fall. The spring shift and the fall decrease of discharge imply an extension of the hydrological dry season and thus an increase in water stress over the basin. The spatial distributions of the discharge changes are in agreement with what is observed at the outlet point and the uncertainty associated with these changes is proportional to the amplitude of the signal. The analysis of the changes in the anomaly distribution of discharge shows that both the increases and decreases in seasonal discharge are tied to the changes in the tails of the distribution, i.e. to the increase or decrease of extreme events.

Analysis of surface ozone using a recurrent neural network

Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet.

Mapping of Alternaria and Pleospora concentrations in Central Italy using meteorological forecast and neural network estimator

Airborne particles (pollens and fungal spores) are recognized as important causes of allergies and many other pathologies whose main symptoms are usually associated with respiratory problems. In addition, these particles seem to be responsible for clinical symptoms of oculorhinitis and bronchial asthma. Many authors showed how pollen and spore concentrations are critically linked to meteorological conditions, while other studies investigated the possibility to estimate these concentrations through meteorological parameters. So, many different approaches have been proposed, and one of the most sophisticated is based on the use of a complex artificial neural network architecture. Once the neural device is calibrated using simultaneous time series of observed meteorological parameters and airborne biological particles, it is straightforward to use the Neural Network to predict spore concentrations using operational Limited Area Meteorological Model. In a previous work, it has been shown that the MM5 meteorological model developed by National Center for Atmospheric Research and Pennsylvania State University can be coupled with the above-cited neural predictor to provide a good prediction of Alternaria and Pleospora spore in the location of L’Aquila (Central Italy). Following the same approach, this work aims to provide the mapping of spore concentration over a wide area covered by high-resolution meteorological prediction in Central Italy. The complex patterns of fungal spore concentrations in selected areas will be described, and the high temporal variability of such fields will be discussed as well. The possibility to infer useful information from the predicted pattern of spore concentrations is discussed, as an example it appears that for people suffering from allergy to fungal spores is more comfortable to spend summertime close to the east coast of Italian Peninsula respect to the west coast. A further step of this work may easily lead to an operational use of the model for supporting the clinical management of allergies and for establishing a preventive strategy in agriculture to avoid unsafe and useless pollution of atmosphere, crops and fields.

Cetemps Hydrological Model (CHyM), a Distributed Grid-Based Model Assimilating Different Rainfall Data Sources

Within the activities of Cetemps Center of Excellence of University of L’Aquila, a distributed grid based hydrological model has been developed with the aim to provide a general purpose tool for flood alert mapping. One of the main characteristic of this model is the ability to assimilate different data sources for rebuilding two dimensional rainfall distribution. The model can be used for any geographical domain with any resolution up to the resolution of the implemented Digital Elevation Model, namely about 300 meters in the current implementation. A Cellular Automata based algorithm has been implemented to extract a coherent drainage network scheme for any geographical domain. The algorithm for flow scheme extraction and for the assimilation of different rainfall data sources are described in details too. Several applications of such algorithms are also shown.

Small-catchment flood forecasting and drainage network extraction using computational intelligence

Forecast, detection and warning of severe weather and related hydro-geological risks is becoming one of the major issues for civil protection. The use of computational intelligence techniques such as artificial neural network and cellular automata algorithm can be suitable for such problems especially for real time forecasting system. Nowcasting (short-term forecasting) of extreme rainfall events is an example that invites to exploit remote sensing systems from satellites, such geostationary and low-orbit radiometers. A rainfall estimation algorithm based on artificial neural network has been developed for this purpose. Satellite data sources can be globally provided but at a quite coarse spatial resolution, therefore, the coupling of rain remote sensing data with regional raingauge networks is also essential for ensuring a calibration of remotely sensed rainfall fields in terms of ground effects. An overwhelming issue is the spatial integration of these rainfall data sources having different space-time resolution and variable accuracies. In this work a cellular automata based algorithm has been used to integrate these heterogeneous data. A flood forecast chain, developed at the Centre of Excellence for Remote Sensing and Hydro-Meteorology Modelling and based on coupled mesoscale atmospheric model and distributed hydrological model with in-situ and remote sensing data integration is presented, with the emphasis on the integration of numerical models and retrieval algorithms using integrated tools based on computational intelligence techniques.

Numerical Experiments to Study the Possible Meteorological Changes Induced by the Presence of a Lake

The Lake Fucino was the largest reservoir of fresh water in the Abruzzo Region until it was drained at the end of last century. The surface of the lake was about 150 square km. Temperature and precipitation historical records show appreciable changes in these variable that could be related to the draining of the lake. Changes in the vegetation around the lake are also recorded especially concerning the existence of plantations of olive trees. The setting of the lake is peculiar being at the center of a closed valley. To assess the possible effect of the lake on the local climate and the meteorology regime we have carried out a simulation using the climate version of the MM5 limited area model. This model includes a code for the soil atmosphere interactions. Also the model has been updated for the land use/land cover at very high resolution (1km). The simulations are carried out at different resolution and use the nesting technique. Preliminary results seem to indicate an overestimation of the changes with respect those present in the historical records.