G. La Loggia

Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy

Abstract The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms. In this paper different spatial interpolation algorithms have been evaluated to produce a reasonably good continuous dataset bridging the gaps in the historical series. The algorithms used are deterministic methods such as inverse distance weighting, simple linear regression, multiple regression, geographically weighted regression and artificial neural networks, and geostatistical models such as ordinary kriging and residual ordinary kriging. In some of these methods, the elevation information, provided by a Digital Elevation Model, has been added to improve estimation of missing data. These algorithms have been applied to the mean annual and monthly rainfall data of Sicily (Italy), measured at 247 raingauges. Optimization of different settings of the various interpolation methods has been carried out using a subset of the available rainfall dataset (modeling set) while the remaining subset (validation set) has been used to compare the results obtained by the different algorithms. Validation results indicate that the univariate methods, neglecting the information of elevation, are characterized by the largest errors, which decrease when the elevation is taken into account. The ordinary kriging of residuals from linear regression between precipitation and elevation, which has provided the best performance at annual and monthly scale, has been used to complete the precipitation monthly time series in Sicily.

Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation.

Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly because a large part of the total uncertainty is dependent on depth-damage curves. Improving the estimation of these curves may provide better results in term of uncertainty reduction than the adoption of detailed hydraulic models.

Evaluation of the apparent losses caused by water meter under-registration in intermittent water supply.

Apparent losses are usually caused by water theft, billing errors, or revenue meter under-registration. While the first two causes are directly related to water utility management and may be reduced by improving company procedures, water meter inaccuracies are considered to be the most significant and hardest to quantify. Water meter errors are amplified in networks subjected to water scarcity, where users adopt private storage tanks to cope with the intermittent water supply. The aim of this paper is to analyse the role of two variables influencing the apparent losses: water meter age and the private storage tank effect on meter performance. The study was carried out in Palermo (Italy). The impact of water meter ageing was evaluated in laboratory by testing 180 revenue meters, ranging from 0 to 45 years in age. The effects of the private water tanks were determined via field monitoring of real users and a mathematical model. This study demonstrates that the impact on apparent losses from the meter starting flow rapidly increases with meter age. Private water tanks, usually fed by a float valve, overstate meter under-registration, producing additional apparent losses between 15% and 40% for the users analysed in this study.

A model of the filling process of an intermittent distribution network

In many countries, private tanks are acquired by users to reduce their vulnerability to intermittent supply. The presence of these local reservoirs modifies the user demand pattern and usually increases user water demand at the beginning of the service period depending on the tank filling process. This practice is thus responsible for the inequality that occurs among users: those located in advantaged positions of the network are able to obtain water resources soon after the service period begins, while disadvantaged users have to wait much longer, after the network is full. This dynamic process requires the development of ad hoc models in order to obtain reliable results. This paper discusses a numerical model used for evaluating this complex process as well as the application of model to an Italian case study. The model agreed with calibration data and provided interesting insights into the network filling process.

Critical analysis of thermal inertia approaches for surface soil water content retrieval

Abstract The “thermal inertia” method to retrieve surface soil water content maps on bare or sparsely-vegetated soils is analysed. The study area is a small experimental watershed, where optical and thermal images (in day and night time) and in situ data were simultaneously acquired. The sensitivity of thermal inertia to the phase difference between incoming radiation and soil temperature is demonstrated. Thus, to obtain an accurate value of the phase difference, the temporal distance between thermographs using a three-temperature approach is evaluated. We highlight when a cosine correction of the temperature needs to be applied, depending on whether the thermal inertia formulation includes two generic acquisition times, or not. Finally, the deviation in soil water content retrieval is quantifies for given values of each parameter by performing a sensitivity analysis on the basic parameters of the thermal inertia method that are usually affected by calibration errors. Citation Maltese, A., Bates, P.D., Capodici, F., Cannarozzo, M., Ciraolo, G., and La Loggia, G., 2013. Critical analysis of thermal inertia approaches for surface soil water content retrieval. Hydrological Sciences Journal, 58 (5), 1144–1161. Editor D. Koutsoyiannis; Associate editor D. Hughes

Co-evolution of hydrological components under climate change scenarios in the Mediterranean area

The Mediterranean area is historically characterized by high human pressure on water resources. Today, while climate is projected to be modified in the future, through precipitation decrease and temperature increase, that jointly and non-linearly may affect runoff, concerns about water availability are increasing. For these reasons, quantitative assessment of future modifications in the mean annual water availability are important; likewise, the description of the future interannual variability of some hydrological components such as runoff and evapotranspiration are highly wished for water management and ecosystems dynamics analyses. This study investigates at basin spatial scale future runoff and evapotranspiration, exploring their probability density functions and their interdependence as functions of climatic changes. In order to do that, a parsimonious conceptual lumped model is here used. The model is forced by different future climate scenarios, generated through a weather generator based on a stochastic downscaling of an ensemble of General Circulation Models (GCMs) realizations. The use of the adopted hydrological model, under reliable stochastic future climate scenarios, allows to project future values of evapotranspiration and runoff in a probabilistic framework and, at the same time, the evaluation of their bivariate frequency distributions for changes through the Multivariate Kernel Density Estimation method. As a case study, a benchmark Mediterranean watershed has been proposed (Imera Meridionale, Italy). Results suggest a radical shift and shape modification of the annual runoff and evapotranspiration probability density functions. Possible implications and impacts on water resources management are here addressed and discussed.

Influence of hydrodynamic conditions on the production and fate of Posidonia oceanica in a semi-enclosed Shallow Basin (stagnone di marsala, Western Sicily)

An integrated approach using hydrodynamic and transport numerical models, lepidochronology and stable isotope analysis was used to investigate how local hydrodynamic conditions influence the primary production and fate of the seagrass Posidonia oceanica in a Mediterranean semi-enclosed marine system (Stagnone di Marsala). The water mass exchange aptitude of different sectors of the basin was analysed, and data collected were used to select two sectors (colonized by Posidonia oceanica showing the lowest and highest water exchange values) for biological analyses. According to the mean dispersal coefficient differences simulated by the hydrodynamic model, growth rate and primary production of P. oceanica differed between sectors, with average values lower in the central sector where water exchange is lower than in the southern sector. Although P. oceanica coverage and primary production were higher in the southern sector, carbon and nitrogen stable isotope analysis suggests that the transfer of seagrass organic matter to higher trophic levels of the food web was higher in the central sector. The possibility of a link between hydrodynamism, production and fate of organic matter is proposed to explain the observed patterns.

Coupling two radar backscattering models to assess soil roughness and surface water content at farm scale

Abstract Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r 2 up to ˜0.8) and soil water content (r 2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Capodici, F., Maltese, A., Ciraolo, G., La Loggia, G., and D’Urso, G., 2013. Coupling two radar backscattering models to assess soil roughness and surface water content at the farm scale. Hydrological Sciences Journal, 58 (8), 1677–1689.

Surface soil humidity retrieval by means of a semi-empirical coupled SAR model

In the last years, the availability of new technologies of Earth Observation encouraged researches to use integrated approaches for environmental monitoring. Even for agro-hydrological applications, remotely sensed data are available on wide areas allowing the retrieval of cost-effective and representative estimation of high spatial and temporal variability of the soil-vegetation system variables. In particular, soil water content plays an important role determining the partition of precipitation between surface runoff and infiltration and, moreover, influences the distribution of the incoming radiation between latent and sensible heat flux. As a consequence, distributed soil water content maps are essential data for watershed applications such as flood prediction and crop irrigation scheduling. Since cloud cover has been highlighted as the main limitation of SW/TIR traditional techniques, this research is focused on the applicability of soil moisture models based on active microwave. In particular, a Semi Empirical Coupled Model (SECM) is proposed. Reliable assessments of both surface roughness and dielectric constant (thus soil moisture) are retrieved by means of two iterative modules, without any calibration phase. The validation with in situ soil moisture, taken at a depth comparable to the RADAR penetration, gives a good agreement for bare-sparse vegetation coverage. The research is carried out on the 24 km² test-site of DEMMIN (Görmin farm, Mecklenburg Vorpommern), in the North-East of Germany. Data were acquired within the ESA-funded AgriSAR project, between April and July 2006. The implemented model uses HH, VV and HV polarized L-bands, acquired by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt - DLR) using an airborne platform.

Identification of the best flood retrofitting scenario in an urban watershed by means of a Bayesian Decision Network

Urban resilience to floods can be defined as a city’s capacity to avoid damage through the implementation of structural and non-structural measures, to reduce damage in the case of a flood that exceeds a desired threshold, to recover quickly to the same or an equivalent state, and to adapt to an uncertain future. To build flood resilience, planners need to identify and analyse risk, to understand the impacts of flooding, and how they cope with these impacts by means of innovative and adaptable strategies and measures. The number of possible retrofitting scenarios to cope with flooding problems in an urban watershed could be greatly increased by the combination of several stormwater management practices. Therefore, the present study aims to develop an expert system in the form of a Bayesian Decision Network (BDN) able to evaluate the efficiency of some possible urban flood retrofitting scenario by examining all significant water management variables and their inherent uncertainty. The methodology was applied to an urbanized area of the city of Palermo (Italy).