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Abstract:The uncertainty in a hydrological model, due to its structure or implemented input parameters, affects the accuracy of simulations that are usually used for important applications such as drought predictions, flood risk assessment, irrigation scheduling, ground water recharge and contamination. Several models describing soil infiltration processes have been developed. Some are analytical, while others implement numerical solutions of the Richards' equation. The objective of this work was to assess the impact of infiltration process modeling on soil water content simulations. For this study, different infiltration models were included within FEST-WB (Flash Flood Event-based Spatially-distributed rainfall-runoff Transformations-Water Balance) distributed hydrological model (SCS-CN, Green and Ampt, Philip and Ross solution). Performances of implemented infiltration models in simulating soil water content were evaluated against observations acquired in the experimental site located in a maize field in northern Italy. Soil water content was monitored together with continuous measurements of meteorological data. A sensitivity analysis was performed to assess the most important parameters governing infiltration process in the different models tested. A comparison of soil water content simulations show that Ross solution allowed the description of soil moisture variation along the vertical, but simpler lumped models provide sufficient accuracy when properly calibrated.

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. Meeting and optimizing the consumption of water for irrigation also means making more resources available for drinking water and industrial use, and maintaining an optimal state of the environment. In this study we show the effectiveness of the combined use of numerical weather predictions and hydrological modelling to forecast soil moisture and crop water requirement in order to optimize irrigation scheduling. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations from space and unconventional information from the cyberspace through crowdsourcing.

Congenital adrenal hyperplasia (CAH) describes a group of autosomal recessive disorders where there is impairment of cortisol biosynthesis. CAH due to 21-hydroxylase deficiency accounts for 95% of cases and shows a wide range of clinical severity. Glucocorticoid and mineralocorticoid replacement therapies are the mainstays of treatment of CAH. The optimal treatment for adults with CAH continues to be a challenge. Important long-term health issues for adults with CAH affect both men and women. These issues may either be due to the disease or to steroid treatment and may affect final height, fertility, cardiometabolic risk, bone metabolism, neuro-cognitive development and the quality-of-life. Patients with CAH should be regularly followed-up from childhood to adulthood by multidisciplinary teams who have knowledge of CAH. Optimal replacement therapy, close clinical and laboratory monitoring, early life-style interventions, early and regular fertility assessment and continuous psychological management are needed to improve outcome.