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Access to water has been and remains one of humanity’s greatest challenges. Especially in arid plains exposed to significant climatic fluctuations and future global change trends. In the past and present, local communities of the arid plains of central-western Argentina (i.e., Guanacache Lagoons, Cuyo region) have developed multiple strategies to manage water supply problems. The aims of this study are: i) to characterize the different water harvesting technologies (pre-Hispanic and modern) used, and ii) to compare the small local strategies of water harvesting (bottom-up solutions) with the large centralized projects (top-down solutions). On the one hand, we show the transformations of these technologies over time, and the challenges faced by inhabitants in the context of climate change trends. On the other hand, we analyze the role of the state through hydraulic policies and projects implemented by the provincial states over the last two centuries and how this impacted the study area. This review is based on a historical and archaeological bibliography, and recent publications about the region, including articles based on our ethnographic fieldwork. Our results demonstrate the valuable experience accumulated by local populations in water harvesting methods, particularly in areas where groundwater is deep and saline, and shows the adaptability of these technologies in contexts of increasing scarcity. We considered that local indigenous knowledge can largely contribute to the sustainable management of water resources. This study might be useful for decision-makers and water managers in drylands around the world to find and equitable approach that combines technical advances with local knowledge.
Access to water has been and remains one of humanity’s greatest challenges. Especially in arid plains exposed to significant climatic fluctuations and future global change trends. In the past and present, local communities of the arid plains of central-western Argentina (i.e., Guanacache Lagoons, Cuyo region) have developed multiple strategies to manage water supply problems. The aims of this study are: i) to characterize the different water harvesting technologies (pre-Hispanic and modern) used, and ii) to compare the small local strategies of water harvesting (bottom-up solutions) with the large centralized projects (top-down solutions). On the one hand, we show the transformations of these technologies over time, and the challenges faced by inhabitants in the context of climate change trends. On the other hand, we analyze the role of the state through hydraulic policies and projects implemented by the provincial states over the last two centuries and how this impacted the study area. This review is based on a historical and archaeological bibliography, and recent publications about the region, including articles based on our ethnographic fieldwork. Our results demonstrate the valuable experience accumulated by local populations in water harvesting methods, particularly in areas where groundwater is deep and saline, and shows the adaptability of these technologies in contexts of increasing scarcity. We considered that local indigenous knowledge can largely contribute to the sustainable management of water resources. This study might be useful for decision-makers and water managers in drylands around the world to find and equitable approach that combines technical advances with local knowledge.
In applications such as environmental monitoring, algorithms and deep learning-based methods using synthetic aperture radar (SAR) and electro-optical (EO) data have been proposed with promising results. These results have been achieved using already cleaned datasets for training data. However, in real-world data collection, data are often collected regardless of environmental noises (clouds, night, missing data, etc.). Without cleaning the data with these noises, the trained model has a critical problem of poor performance. To address these issues, we propose the Clean Collector Algorithm (CCA). First, we use a pixel-based approach to clean the QA60 mask and outliers. Secondly, we remove missing data and night-time data that can act as noise in the training process. Finally, we use a feature-based refinement method to clean the cloud images using FID. We demonstrate its effectiveness by winning first place in the SAR-to-EO translation track of the MultiEarth 2023 challenge. We also highlight the performance and robustness of the CCA on other cloud datasets, SEN12MS-CR-TS and Scotland&India.
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