244 research outputs found

    Assessing downscaling techniques for frequency analysis, total precipitation and rainy day estimation in CMIP6 simulations over hydrological years

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    General circulation models generate climate simulations on grids with resolutions ranging from 50 to 600 km. The resulting coarse spatial resolution of the model outcomes requires post-processing routines to ensure reliable climate information for practical studies, prompting the widespread application of downscaling techniques. However, assessing the effectiveness of multiple downscaling techniques is essential, as their accuracy varies depending on the objectives of the analysis and the characteristics of the case study. In this context, this study aims to evaluate the performance of downscaling the daily precipitation series in the Metropolitan Region of Belo Horizonte (MRBH), Brazil, with the final scope of performing frequency analyses and estimating total precipitation and the number of rainy days per hydrological year at both annual and multiannual levels. To develop this study, 78 climate model simulations with a horizontal resolution of 100 km, which participated in the SSP1-2.6 and/or SSP5-8.5 scenarios of CMIP6, are employed. The results highlight that adjusting the simulations from the general circulation models by the delta method, quantile mapping and regression trees produces accurate results for estimating the total precipitation and number of rainy days. Finally, it is noted that employing downscaled precipitation series through quantile mapping and regression trees also yields promising results in terms of the frequency analyses.</p

    Strength of Protection for Geographical Indications: Promotion Incentives and Welfare Effects

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    We address the question of how the strength of protection for geographical indications (GIs) affects the GI industry\u27s promotion incentives, equilibrium market outcomes, and the distribution of welfare. Geographical indication producers engage in informative advertising by associating their true quality premium (relative to a substitute product) with a specific label emphasizing the GI\u27s geographic origin. The extent to which the names/words of the GI label can be used and/or imitated by competing products—which depends on the strength of GI protection—determines how informative the GI promotion messages can be. Consumers’ heterogeneous preferences (vis-à-vis the GI quality premium) are modeled in a vertically differentiated framework. Both the GI industry and the substitute product industry are assumed to be competitive (with free entry). The model is calibrated and solved for alternative parameter values. Results show that producers of the GI and of the lower-quality substitute good have divergent interests: GI producers are better off with full protection, whereas the substitute good\u27s producers prefer intermediate levels of protection (but they never prefer zero protection because they benefit indirectly if the GI producers’ incentives to promote are preserved). For consumers and aggregate welfare, the preferred level of protection depends on the model\u27s parameters, with an intermediate level of protection being optimal in many circumstances

    Sensors prioritisation for hydrological forecasting based on interpretable machine learning

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    The digitalisation of the hydrological sector introduces new challenges related to IoT network implementation, extensive data management, and real-time analysis while offering significant opportunities to improve hydrological forecasts. Reliable information is crucial for managing hydrogeological risks and optimising water usage, particularly in the current era of climate change, marked by frequent and severe extreme events such as intense precipitation and prolonged droughts. This study aims to enhance short-term hydrological predictions by prioritising sensors based on interpretable machine learning. We propose an evaluation framework that involves tuning machine learning-based hydrological models for different horizons, applying leave-one-out cross-validation to simulate sensor failures and evaluate their significance, and defining sensor priority levels. Conducted in the South Tyrol watershed (northern Italy), this study uses data from streamflow gauges and weather stations. The results show that specific sensors significantly impact forecasting accuracy, and prioritisation improves the reliability of hydrological predictions. These findings highlight the importance of maintaining critical sensors and provide a data-driven methodology for optimising resource allocation in monitoring system maintenance, ultimately enhancing the robustness of hydrological forecasting and risk mitigation strategies
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