Salvatore Manfreda

Representation of space–time variability of soil moisture

A simplified spatial-temporal soil moisture model driven by stochastic spatial rainfall forcing is proposed. The model is mathematically tractable, and allows the spatial and temporal structure of soil moisture fields, induced by the spatial-temporal variability of rainfall and the spatial variability of vegetation, to be explored analytically. The influence of the main model parameters, reflecting the spatial scale of rain cells, the soil storage capacity, the rainfall interception and the soil water loss rate (representing evaporation and deep infiltration) is investigated. The variabilities of the spatially averaged soil moisture process, and that averaged in both space and time, are derived. The present analysis focuses on spatially uniform vegetation conditions; a follow-up paper will incorporate stochastically heterogeneous vegetation.

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

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.

Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle

ABSTRACT To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.

DEM-Based Approaches for the Delineation of Flood-Prone Areas in an Ungauged Basin in Africa

AbstractIn the present work, the flood hazard exposure in an ungauged basin in Africa is assessed exploiting the basin morphological characteristics. Flood-prone areas are identified using linear binary classifiers based on several geomorphic descriptors extracted from digital elevation models (DEMs). The classifiers are calibrated individually and evaluated by comparing their outputs with a flood inundation map obtained by two-dimensional (2D) hydraulic simulations and using receiver operating characteristics (ROC) curves as performance measures. The best-performing descriptors for the subcatchment of the Bulbula River, near the city of Addis Ababa (Ethiopia), are the elevation difference, H between the location under exam and the nearest drainage network, and the composite index ln[hr/H], that compares an estimate of the water level in the nearest point of the river network to the difference in elevation between the point under exam and the river. These simple procedures allow extending the flood deline...

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

Flood-Prone Areas Assessment Using Linear Binary Classifiers based on Morphological Indices

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 relay 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 DEMderived morphologic features. 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. The analyses highlight the potential of each morphological descriptor for the identification of the extend of flood-prone areas. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures.

Watershed influence on fluvial ecosystems: an integrated methodology for river water quality management

The EU Water Framework Directive 2000/60 (Integrated River Basin Management for Europe) establishes the importance of preserving water quality through policies applied at watershed level given the strong links existing among ecological, hydrological, and hydrogeological systems. Therefore, monitoring campaigns of river water quality should be planned with multidisciplinary approaches starting from a landscape perspective. In this paper, the effects of the basin hydrology on the river water quality and, in particular, the impacts caused by the runoff production coming from agricultural areas are investigated. The fluvial segments receiving consistent amount of pollutant loads (due to the runoff routing over agricultural areas) are assumed more critical in terms of water quality and thus, they require more accurate controls. Starting from this perspective, to evaluate the runoff productions coming from agricultural areas, we applied a semi-distributed hydrological model that adopts satellite data, pedological and morphological information for the watershed description. Then, the river segments receiving critical amount of runoff loads from the surrounding cultivated areas were identified. Finally, in order to validate the approach, water quality for critical and non critical segment was investigated seasonally, by using river macroinvertebrates as indicators of water quality because of their effectiveness in preserving in time a memory of pollution events. Biomonitoring data showed that river water quality strongly decreases in correspondence of fluvial segments receiving critical amount of runoff coming from agricultural areas. The results highlight the usefulness of such a methodology to plan monitoring campaigns specifically devoted to non-point pollution sources and suggest the possibility to use this approach for water quality management and for planning river restoration policies.

A model of bridge pier scour during flood waves

ABSTRACT The time-dependent bridge pier scour during flood waves is analysed. Scour experiments were conducted in a novel installation able to produce complex hydrographs with high precision. Experimental data were used to test scour formulas including a new mathematical model. Results confirm the reliability and superior performance of the proposed dimensionless, effective flow work based model under steady and unsteady hydraulic conditions. Analyses highlight the impact of different hydrographs on scour, demonstrating the strong control by the hydrograph shape of the temporal evolution of scour depth and scour rate, although final scour after a flood only depends on the total effective flow work exerted by the hydrograph on the sediment bed. Hysteresis between flow discharge and scour rate is reported and explained. Flow acceleration is shown to play a minor role in scouring. The proposed model is a promising alternative for computation of local scour under highly unsteady hydraulic conditions.

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.