ABSTRACT
In modern urban areas, water management highly depends on the socio-ecological urban water cycle (UWC) that heavily relies on water infrastructures. However, increasing water-related hazards, natural and/or human-based, makes it difficult to balance water resources in the socio-ecological UWC. In the last decade, urban infrastructure resilience has rapidly become a popular topic in disaster risk management and inspired many studies and operational approaches. Among these theories and methods, the "Behind the Barriers" model (BB model), developed by Barroca and Serre in 2013, is considered a theory that allows effective and comprehensive analysis of urban infrastructure resilience through cognitive, functional, correlative, and organisational dimensions. Moreover, this analysis can be a reference to develop actions that improve infrastructure resilience under critical scenarios. Therefore, this study aims to study resilience design actions based on the BB model to achieve socio-ecological water balance and assess the performance of these actions. The study focuses on water management on a neighbourhood scale, which is considered the essential urban unit to study and improve the resilience of critical infrastructures, such as water services. The Part-Dieu neighbourhood in Lyon, France is selected as a case study, and it highlights the need to develop indicators to assess the performance of implemented actions in a structural and global resilience framework, to understand urban systems as complex and dynamic systems to provide decision support, and to strengthen crisis prevention and management perspectives in a dynamic approach.
Subject(s)
Disasters , Water , Humans , Water Supply , Water Resources , FranceABSTRACT
Irrigation has traditionally been managed as uniform applications where an entire field receives the same depth of water. Motivation to improve current irrigation practices has led to different approaches utilizing remotely-sensed images to inform variable rate irrigation management. This study conducted in 2019 and 2020 implemented the Spatial EvapoTranspiration Modeling Interface (SETMI), a remote-sensing-based evapotranspiration (ET) and water balance model, for managing variable rate irrigation of a maize and soybean field. This model tracked soil water content through the estimation of daily ET and tracking of various water fluxes entering and leaving a field. SETMI was used in two different irrigation treatments informed using Planet satellite (SETMI-SAT) and unmanned aerial system (UAS, SETMI-UAS) remotely-sensed images. A uniform irrigation approach managed by a professional crop consultant and a non-irrigated approach were used as the baseline in comparing irrigation management approaches. The irrigation treatments were evaluated on dry grain yield, gross irrigation, actual ET, deep percolation, change in soil water content, and water productivity. The uniform irrigation approach managed by the crop consultant applied the highest irrigation in 2019 and 2020 for maize (2019: 155 mm, 2020: 213 mm) and soybean (2019: 124 mm;2020: 183 mm) while the SETMI irrigation treatments applied less irrigation for maize (2019: 131, 132 mm;2020: 154, 140 mm) and soybean (2019: 116, 94 mm;2020: 154, 175 mm). Maize yield was highest for the uniform irrigation approach in 2019 (14.9 Mg ha−1) and 2020 (13.3 Mg ha−1). The highest soybean yield was produced by the SETMI-SAT irrigation management approach in 2019 (5.0 Mg ha−1) and 2020 (4.8 Mg ha−1). Significant differences (p-value < 0.05) in applied irrigation between the uniform and SETMI irrigation management approaches were observed while there were no significant differences in dry grain yield for both maize and soybean in 2019 and 2020. At least one of the SETMI irrigation treatments produced higher crop, irrigation, and ET water productivity values in comparison to those produced by the uniform irrigation treatment for all crop-years. A post-season analysis was completed using the SETMI-UAS and SETMI-SAT treatments to evaluate the accuracy of estimated rootzone soil water depletion provided by SETMI. Rootzone depletion calculated from neutron probe volumetric soil water content measurements were compared to the modeled depletion from the SETMI-UAS and SETMI-SAT treatments. The 2020 modeled and measured depletion comparison produced better agreement resulting in a root mean squared error (RMSE) < 17 mm compared to 2019 (RMSE < 27 mm). The VRI center pivot malfunctioned during the 2019 season which caused unresolved discrepancies between actually applied irrigation and what the system was programmed to apply. The VRI system was fixed before the 2020 season. • Remotely-sensing-based evapotranspiration model can improve irrigation management. • Variable rate irrigation can be effective informed through remote sensing. • Variable rate irrigation can decrease applied irrigation while maintaining crop yields. [ FROM AUTHOR] Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Dry air alters salt and water balance in the upper airways and increases the risks of COVID-19 among other respiratory diseases. We explored whether such upper airway variations in salt and water balance might alter respiratory droplet generation and potentially contribute to observed impacts of airway hydration on respiratory disease. In a randomized 4-arm study of 21 healthy human subjects we found that the breathing of humid air, the wearing of cotton masks, and the delivery of (sodium, calcium, and magnesium chloride) salt droplets sized to deposit in the nose, trachea, and main bronchi similarly reduce the exhalation of respiratory droplets by approximately 50% (P < 0.05) within 10 minutes following hydration. Respiratory droplet generation returns to relatively high baseline levels within 60–90 minutes on return to dry air in all cases other than on exposure to divalent (calcium and magnesium) salts, where suppression continues for 4–5 hours. We also found via a preliminary ecological regression analysis of COVID-19 cases in the United States between January 2020 and March 2021 that exposure to elevated airborne salt on (Gulf and Pacific) US coastlines appears to suppress by approximately 25%–30% (P < 0.05) COVID-19 incidence and deaths per capita relative to inland counties — accounting for ten potential confounding environmental, physiological, and behavioral variables including humidity. We conclude that the hydration of the upper airways by exposure to humidity, the wearing of masks, or the breathing of airborne salts that deposit in the upper airways diminish respiratory droplet generation and may reduce the risks of COVID-19 incidence and symptoms.
ABSTRACT
In this paper, the FEST-FOREST model is presented. A FOREST module is written in the FORTRAN-90 programming language, and was included in the FEST-WB distributed hydrological model delivering the FEST-FOREST model. FEST-FOREST is a process-based dynamic model allowing the simulation at daily basis of gross primary production (GPP) and net primary production (NPP) together with the carbon allocation of a homogeneous population of trees (same age, same species). The model was implemented based on different equations from literature, commonly used in Eco-hydrological models. This model was developed within the framework of the INNOMED project co-funded under the ERA-NET WaterWorks2015 Call of the European Commission. The aim behind the implementation of the model was to simulate in a simplified mode the forest growth under different climate change and management scenarios, together with the impact on the water balance at the catchment. On a first application of the model, the results are considered very promising when compared to field measured data.