Evapotranspiration (ET), a key variable in both water and energy cycles. It is very challenging to measure or estimate in large regions. Among many approaches to estimate ET indirectly (e.g. through hydrological modelling), models that are based on satellite remote sensing data (RS) are increasingly being used. However, the RS-based models inherit uncertainty from many sources, such as the model’s algorithm and parameters, input satellite data, and processing techniques. It is challenging to assess this uncertainty due to limitations of validation data, high volume and high dimensionality of RS data. Many studies have evaluated uncertainty in RS-based estimation of ET using different methods and reference data. The suitability of methods and reference data subsequently affect the validity of these evaluations. Therefore, it is necessary to have an overview of different evaluation methods and their uses. This study aimed to systematically review original research papers that assessed uncertainty or accuracy of RS-ET model or data products. We categorized these papers and quantified based on (i) spatial and temporal scale of ET estimation, (ii) types of uncertainty, and (iii) methods used to assess uncertainty. Studies have been geographically concentrated in North Asia, North America, and Europe. Most studies used the validation method, which quantifies the discrepancy between pixel-based ET estimation with an in-situ estimation. Although a standardized validation approach for satellite-based ET estimates is not yet ready, most validation studies employed Eddy Covariance (EC) flux towers for reference estimation at field-scale. In regions where in-situ measurements are limited, many studies use the residual of the water balance as reference. However, few studies considered uncertainty in the reference estimation and mismatch of spatial and temporal scales. For monitoring agricultural fields, most RS-ET methods have been reported with high accuracy. When applying these methods to larger extent, additional assessments are required to better inform data users of the quality of RS-ET estimation. These include cross-validation, sensitivity, and uncertainty analyses. Overall, this review showed the progress in evapotranspiration estimation using satellite data in terms of uncertainty assessment.

Waternomics is a European Union-funded research project aspiring to develop and introduce Information and Communication Technology (ICT) as an enabling technology to manage water as a resource, increase end-user conservation awareness, affect behavioural changes and avoid water losses through leak detection. Existing leakage detection methods are generally focused on scrutinising large diameter pipes in water supply distribution networks or transmission pipes. However, it has been estimated that the average household's leaks can be as much as 35m³ of water per year. In order to solve the problem, analysis of different types of data in the household piping system is required, including detection and identification. One conventional approach is to use flow sensors installed at several locations within the household piping system and perform a mass balance approach to detect leakage. However, this method is expensive and difficult to implement. This research proposes a novel approach to household leakage detection by means of sound signal recordings. The approach consists of recording the sound signals that are produced by water fixtures and appliances, and then use these recordings to detect any abnormal situation which may be an indication of a leak. The method comprises three major steps: recording, storing and processing of sound signals. The recording step is done by means of a non-intrusive sound sensor that sends records remotely; the storage step is made in a database of sound signals for different types of uses; finally, the processing step is made through a sound signal identification software tool that is able to search the database libraries for related sounds, in a similar way as the Shazam app for music. Tests of the leak detection method are presented for data collected in laboratory conditions. Results show that this detection method has a potential to help reducing leakages through an easy-to-install and non-intrusive sensor.

Recent research suggests future alterations in rainfall patterns due to climate variability, affecting public safety and health in urban areas. Urban growth, one of the main drivers of change in the current century, will also affect these conditions. Traditional drainage approaches using grey infrastructure offer low adaptation to an uncertain future. New methodologies of stormwater management focus on decentralized approaches in a long-term planning framework, including the use of Green Infrastructure (GI). This work presents a novel methodology to select, evaluate, and place different green-grey practices (or measures) for retrofitting urban drainage systems. The methodology uses a hydrodynamic model and multi-objective optimization to design solutions at a watershed level. The method proposed in this study was applied in a highly urbanized watershed to evaluate the effect of these measures on Combined Sewer Overflows (CSO) quantity. This approach produced promising results and may become a useful tool for planning and decision making of drainage systems.