A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin Water Transfer regimes

Interbasin Water Transfer (IWT) is often a complex decision-making process that depends on factors ranging from hydro-meteorological conditions to socio-economic pressures. Hydrologic modelling is particularly challenging under these circumstances, requiring accurate quantitative information which may not always be available. This study proposes a methodological framework to simulate IWT flow contributions in the absence of observational data by introducing a coupled machine learning–hydrologic modelling approach. The proposed methodology employs a hydrologic model to simulate the rainfall-runoff process of a watershed, while a machine learning algorithm is used to simulate the decision-making process of IWTs. Methods are illustrated by simulating the hydrologic balance of the Dese-Zero River Basin (DZRB), a highly artificially modified catchment located in North-East Italy. Results suggest the proposed methodological framework can successfully simulate the complex water flow dynamics of the studied watershed and be a useful instrument to support complex scenario analysis under IWTs data scarce conditions.Interbasin Water Transfer (IWT) is often a complex decision-making process that depends on factors ranging from hydro-meteorological conditions to socio-economic pressures. Hydrologic modelling is particularly challenging under these circumstances, requiring accurate quantitative information which may not always be available. This study proposes a methodological framework to simulate IWT flow contributions in the absence of observational data by introducing a coupled machine learning–hydrologic modelling approach. The proposed methodology employs a hydrologic model to simulate the rainfall-runoff process of a watershed, while a machine learning algorithm is used to simulate the decision-making process of IWTs. Methods are illustrated by simulating the hydrologic balance of the Dese-Zero River Basin (DZRB), a highly artificially modified catchment located in North-East Italy. Results suggest the proposed methodological framework can successfully simulate the complex water flow dynamics of the studied watershed and be a useful instrument to support complex scenario analysis under IWTs data scarce conditions

Testing empirical and synthetic flood damage models: the case of Italy

Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.</p

Pathogenic Connexin-31 Forms Constitutively Active Hemichannels to Promote Necrotic Cell Death

Mutations in Connexin-31 (Cx31) are associated with multiple human diseases including erythrokeratodermia variabilis (EKV). The molecular action of Cx31 pathogenic mutants remains largely elusive. We report here that expression of EKV pathogenic mutant Cx31R42P induces cell death with necrotic characteristics. Inhibition of hemichannel activity by a connexin hemichannel inhibitor or high extracellular calcium suppresses Cx31R42P-induced cell death. Expression of Cx31R42P induces ER stress resulting in reactive oxygen species (ROS) production, in turn, to regulate gating of Cx31R42P hemichannels and Cx31R42P induced cell death. Moreover, Cx31R42P hemichannels play an important role in mediating ATP release from the cell. In contrast, no hemichannel activity was detected with cells expressing wildtype Cx31. Together, the results suggest that Cx31R42P forms constitutively active hemichannels to promote necrotic cell death. The Cx31R42P active hemichannels are likely resulted by an ER stress mediated ROS overproduction. The study identifies a mechanism of EKV pathogenesis induced by a Cx31 mutant and provides a new avenue for potential treatment strategy of the disease

Beyond piecewise methods: Modular integrated hydroeconomic modeling to assess the impacts of adaptation policies in irrigated agriculture

The accurate understanding of the human-modified water cycle calls for a detailed representation of human and water systems, including relevant non-linearities, and of the feedback responses between them. This paper couples a microeconomic Positive Multi-Attribute Utility Programming model with a Hydrologic Modeling System (HEC-HMS) with the objective of incorporating the behavior and adaptive responses of human agents into the representation of the human-modified water cycle. The coupling occurs in a sequential fashion using bidirectional protocols that represent the feedback responses between the microeconomic and hydrologic modules through common spatial elements and variables. The proposed model is illustrated with an application to agricultural water management in the Upper Tagus River Basin in Spain. The non-linear responses observed in the modeled human-water system suggest that strengthening the agricultural water allocation constraint can avert or delay drought negative environmental impacts with a less-than-proportional yet incremental impact on Gross Value Added and employment