Carmelo Cammalleri

Traditional urban drainage systems face increasing pressures due to growing urbanization and climate change, which amplify stormwater volumes beyond the design capacity of sewer networks. These conditions often result in combined sewer overflows (CSOs) and wastewater treatment plant (WWTP) bypasses, discharging untreated, nutrient-laden effluents into receiving water bodies. Elevated nitrogen (N) and phosphorus (P) loads from these events exacerbate eutrophication and harmful algal blooms in lakes. The ENVISION project (Lake Pollution: Integrating Nature-Based Solutions Into Environmental Urban Planning for Risk Mitigation) addresses the impact of urban discharges on lakes and evaluates the potential of Nature-Based Solutions (NBSs) to mitigate N and P releases from CSOs. A coupled hydrological–hydraulic and probabilistic modeling framework is applied to the Lake Maggiore (Northern Italy) catchment as a case study, to assess the potential of NBSs in reducing CSO events and, consequently, the N and P loads discharged into the lake. Preliminary results from the hydrological–hydraulic approach are presented.

Drought can arguably be considered the most important natural hazard affecting agricultural production worldwide. In rainfed crops, in particular, severe soil water deficit conditions can have direct impacts on crop yields, negatively affecting the local economy. Among rainfed crops, cereals are the most prominent production in Europe, accounting for about 20% of global production.In this study, soil moisture drought conditions modelled following a global scale hydrological model (LISFLOOD) are used to explain cereal yield anomalies recorded over European regions (NUTS2) by Eurostat for the period 1991-2023. Due to the spatio-temporal mismatch between yield records (annual, over NUTS2 regions) and modelled soil moisture (daily, over a regular grid), different strategies are tested to assess the relationship between the two quantities. By focusing on the years affected by drought conditions, and the consequent expected reduction in yield, ranked zero-clustered correlation metrics are used to quantify the correspondence.Over most of the regions, a positive and significant correlation between drought occurrence and yield reduction is observed, even if this is not the case for a few of the study regions. Overall, the temporal aggregation of soil moisture data over different seasons seems to play a major role in strengthening/weakening the relationship between soil moisture drought and yield reduction, with notable spatial patterns in the outcome. The typical European growing season, April-September, corresponds to the optimal case in most of the regions, but both earlier and later seasons (as well as shorter ones) are also observed in a non-negligible fraction of cases. A method to optimize the best aggregation strategy is proposed, by jointly minimizing the number of different solutions and maximizing the rank correlation. This optimization aims at providing a simple approach that can be used to infer the expected yield reductions given the antecedent modelled soil moisture status across European regions.Acknowledgements: This work is partially funded by the European Union under the HORIZON-CL4-2023-SPACE-01-32 project “Strengthening Extreme Events Detection for Floods and Droughts” (SEED-FD), CUP: D43C23003660006 - 2023.

Evapotranspiration (ET) knowledge is crucial for evaluating crop field water budgets and agricultural water resources management. To monitor crop water requirements various data sources are used such as: in situ (meteorological and soil water content data) measurements, reanalysis database, remote sensing observations, and models. Two approaches can be implemented: the Soil Water Balance (SWB) and the Surface Energy Balance (SEB).This research aimed to evaluate these two approaches, by combining in situ or reanalysis meteorological data with remotely sensed images to explore the possible synergies between the approaches to propose an operational ET estimation in the context of future Thermal InfraRed (TIR) missions (TRISHNA and LSTM). With a SWB model, both actual evapotranspiration (ETa) and soil water content (SWC) were daily estimated; whereas, with a SEB model latent heat flux (LE) was instantaneously evaluated.Among the available SWBs, the SAtellite Montoring for Irrigation (SAMIR) is a FAO-2Kc-based model integrating remotely sensed images of vegetation cover for evapotranspiration spatialization and water balance. SAMIR can be forced by irrigation either measured or simulated employing specific rules based on the simulated SWC. Alternatively, the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) is a two-source SEB model driven by remotely sensed Land Surface Temperature (LST) and vegetation cover. Both SWB and SEB were investigated by using different input variable combinations. For SAMIR, two combinations were employed: a) using in situ and b) using ERA5-Land reanalysis meteorological variables to estimate crop reference evapotranspiration and precipitation depth. Both incorporated farmer irrigation scheduling and Sentinel-2 NDVI-derived vegetation cover. For SPARSE, three combinations were employed: a) using in situ meteorological data, LST, and albedo; b) replacing LST and albedo with Landsat-8/9 data; c) replacing in situ data with ERA5-Land reanalysis while maintaining Landsat-8/9 inputs.The experiments occurred during seven irrigation seasons, from 2018 to 2024, in a Mediterranean citrus orchard (Citrus reticulata Blanco cv. Mandarino Tardivo di Ciaculli), located near Palermo, Italy (38° 4’ 53.4’’ N, 13° 25’ 8.2’’ E) in which different irrigation systems and management strategies were applied. The field was equipped with a standard weather station, an Eddy Covariance tower, and four “drill and drop” probes to acquire: meteorological variables, energy fluxes, and SWC, respectively.SAMIR best performance was obtained using the a-combination with Root Mean Square Error (RMSE) always less than 0.54 mm d-1 and 0.02 cm3 cm-3 for ETa and SWC, respectively. These metrics were achieved excluding data from 2021 during which worse metrics (ETa RMSE equal to 0.87 mm d-1) were probably caused by the presence of weeds due to the lack of maintenance provided by the farmer. SPARSE best performance was obtained using a-combination with LE RMSE equal to 53 W m-2. Noticeably, b- and c- combinations were implemented using a limited number of data (contextually to satellites acquisitions) thus achieving worse metrics (RMSE equal to 66 W m-2 and 93 W m-2 for b- and c- combinations, respectively).Satisfactory results gained permit this work to keep on being updated toward the synergies between the approaches for better ET estimation.

Actual evapotranspiration (ET) is commonly the largest extractive term in the land surface water balance, thus representing a key component of any water management activity and water resource quantification. Unfortunately, in-situ ET observations are often expensive, sporadically collected, and representative only of local conditions. In this context, modelling approaches represent a widespread alternative for the characterization of ET over large areas and for log time periods. While most of the spatially-distribute ET estimation approaches relies on satellite data to some extent, not all these estimates can be considered as satellite ET products. Like other satellite-based datasets, ET estimates are indirect in nature, and often depend on modelling approaches characterized by a variety of approaches and input requirements integrating a mixture of satellite and non-satellite datasets. With continuous advancements and developments in satellite data, the number of continental to global satellite ET products are increasing and they are characterized by a vast variety of sensors and modelling methods. This increasing number of available ET products underscores the need for a concerted effort in defining the standards and protocols for validation and evaluation exercises, which is the main goal of the Committee on Earth Observation Satellite (CEOS) Land Product Validation (LPV) subgroup. In this research, an overview of the methodologies adopted for the assessment of satellite-based ET will be provided, with a focus on the key hypotheses and forcings representing the “satellite” component of the approaches. This overview will provide a common reference of what constitute a satellite-based ET product, to be investigated by the CEOS LPV subgroup in the definition of recommended protocols to assess the accuracy and reliability of current and future continental to global satellite ET datasets.