Andrea Gioia

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that rely on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEM-derived morphologic features. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures. With this aim, local features—which are generally used to describe the hydrological characteristics of a basin—and composite morphological indices are taken into account in order to identify the most significant one. Analyses are carried out on two different datasets: one based on flood simulations obtained with a 1D hydraulic model, and the second one obtained with a 2D hydraulic model. The analyses highlight the potential of each morphological descriptor for the identification of the extent of flood-prone areas and, in particular, the ability of one geomorphologic index to represent flood-inundated areas at different scales of application.

Inter-comparison of hydrological model simulations with time series of SAR-derived soil moisture maps

Abstract A comparison between superficial soil moisture content, m, values predicted by the DREAM hydrologic model and those retrieved from time-series of ALOS/PALSAR and COSMO-SkyMed SAR data acquired in 2007 and 2010–2011 is presented. The area investigated is part of the Celone at Ponte Foggia-S. Severo river basin, which is a tributary of the Candelaro river, downstream of the S. Giusto Dam, in Puglia (Southern Italy). Results show a good agreement in terms of bias and rmse between the hydrologic modeled and SAR-retrieved mv-values, and open new opportunities for the use of SAR-derived mv-values to calibrate/validate hydrologic models in semi-arid areas.

Testing high spatial resolution WorldView-2 imagery for retrieving the leaf area index

This work analyzes the potentiality of WorldView-2 satellite data for retrieving the Leaf Area Index (LAI) area located in Apulia, the most Eastern region of Italy, overlooking the Adriatic and Ionian seas. Lacking contemporary in-situ measurements, the semi-empiric method of Clevers (1989) (CLAIR model) was chosen as a feasible image-based LAI retrieval method, which is based on an inverse exponential relationship between the LAI and the WDVI (Weighted Difference Vegetation Index) with relation to different land covers. Results were examined in homogeneous land cover classes and compared with values obtained in recent literature.

On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

A Rationale for Pollutograph Evaluation in Ungauged Areas, Using Daily Rainfall Patterns: Case Studies of the Apulian Region in Southern Italy

In the context of the implementation of sustainable water treatment technologies for soil pollution prevention, a methodology that try to overcome the lack of runoff quality data in Puglia (Southern Italy) is firstly tackled in this paper. It provides a tool to obtain total suspended solid (TSS) pollutographs in areas without availability of monitoring campaigns. The proposed procedure is based on the relationship between rainfall characteristics and pollutant wash-off. In particular, starting from the evaluation of the observed regional rainfall patterns by using a rainfall generator model, the storm water management model (SWMM) was applied on five case studies located in different climatic subareas. The quantity SWMM parameters were evaluated starting from the drainage network and catchments characteristics, while the quality parameters were obtained from results of a monitoring campaign conducted for quality model calibration and validation with reference to the pollutograph’s shape and the peak-time. The research yields a procedure useful to evaluate the first flush phenomenon in ungauged sites and, in particular, it provides interesting information for designing efficient and sustainable drainage systems for first flush treatment and diffuse pollution treatment.

Performance of a Theoretical Model for the Description of Water Balance and Runoff Dynamics in Southern Italy

In the present paper, an analytical work for the description of the soil water balance and runoff production was adopted over a significant number of river basins belonging to a humid region of Southern Italy. The model is based on a stochastic differential equation, where the spatial heterogeneity of a basin is incorporated by a parabolic function describing the distribution of soil water storage capacity at the basin scale. The model provides an analytical description of the probability density function (PDF) of relative saturation of a basin as well as the PDF of daily runoff production. The proposed model includes five parameters that depend on climatic and soil characteristics. In particular, two parameters describe the rainfall process (α and λ), two characterize the distribution of soil water storage capacity (wmax and b), and the last is the soil water loss coefficient (V). Application of the model allowed the regionalization of model parameters based on physi- cally consistent characteristics of the river basins. In particular, it was found that the soil water loss coefficient is strongly controlled by the fraction of forest cover of the river basin, while the parameter b, controlling the shape of the distribution of soil water storage capacity, is influenced by the basin topography. DOI: 10.1061/(ASCE)HE.1943-5584.0000879.

Time series of COSMO-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.

A ground network for SAR-derived soil moisture product calibration, validation and exploitation in Southern Italy

A ground network of 12 stations continuously monitoring soil moisture and temperature at various depths has been recently set up over an experimental site of 4km2 in the Capitanata plain (Southern Italy). The calibration of the instrumentation is in progress. The long-term high resolution ground observations will be well-suited for SAR-derived soil moisture product validation. Moreover, the ground network will be also associated with hydrologic and agricultural model activities, with the aim of combining land process models with Earth Observation for improving land applications, such as flood/drought and crop yield monitoring and forecast. Indeed, the Capitanata plain is a crucial area in the Mediterranean basin for studying the impact of climate changes and anthropogenic pressure on water availability/demand and wheat production.

The Use of Geomorphological Descriptors and Landsat-8 Spectral Indices Data for Flood Areas Evaluation: A Case Study of Lato River Basin

In the last few years, the scientific community has dedicated a strong effort for the rapid identification and mapping of flood risk. Last generation models have often taken advantage (even without of in-situ measurements) of the distributed information provided from remotely sensed data. In this work is proposed a multidisciplinary approach to reproduce maps of flooded areas. The method compared spectral descriptors to estimate the areas at risk of flooding in the Lato river basin (Puglia region - Southern Italy) using the ground effects caused by flood events. The inundated areas, obtained with a 2D hydraulic model, were used as reference for Landsat-8 spectral indices. The selection of the most appropriate spectral index was achieved using the binary classifiers test. Lastly, the adopted procedure provided also the calibration of different geomorphological descriptors for a rapid identification of areas at risk of flooding by using Digital Elevation Models.

Comparison of different methods describing the peak runoff contributing areas during floods

In the last few years, the scientific community has developed several hydrological models aimed at the simulation of hydrological processes acting at the basin scale. In this context, the portion of peak runoff contributing areas represents a critical variable for a correct estimate of surface runoff. Such areas are strongly influenced by the saturated portion of a river basin (influenced by antecedent conditions) but may also evolve during a specific rainfall event. In the recent years, we have developed 2 theoretically derived probability distributions that attempt to interpret these 2 processes adopting daily runoff and flood-peak time series. The probability density functions (PDFs) obtained by these 2 schematisations were compared for humid river basins in southern Italy. Results highlighted that the PDFs of the peak runoff contributing areas can be interpreted by a gamma distribution and that the PDF of the relative saturated area provides a good interpretation of such process that can be used for flood prediction.