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Projeto de investigação
Understanding and Modelling the Earth System with Machine Learning
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Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa
Publication . Küçük, Çağlar; Koirala, Sujan; Carvalhais, Nuno; Miralles, Diego G.; Reichstein, Markus; Jung, Martin; CENSE - Centro de Investigação em Ambiente e Sustentabilidade; DCEA - Departamento de Ciências e Engenharia do Ambiente; Frontiers Media
Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.
Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles
Publication . Poehls, Jeran; Alonso, Lazaro; Koirala, Sujan; Reichstein, Markus; Carvalhais, Nuno; CENSE - Centro de Investigação em Ambiente e Sustentabilidade; DCEA - Departamento de Ciências e Engenharia do Ambiente; Elsevier Science B.V., Amsterdam.
Soil moisture is a key factor that influences the productivity and energy balance of ecosystems and biomes. Global soil moisture measurements have coarse native resolutions of 36km and infrequent revisits of around three days. However, these limitations are not present for many variables connected to soil moisture such as land surface temperature and evapotranspiration. For this reason many previous studies have aimed to discern the relationships between these higher resolution variables and soil moisture to produce downscaled soil moisture products. In this study, we test four ensemble machine learning models for this downscaling task. These models use a dataset of over 1,000 sites across the US to predict soil moisture at sub-km scales. We find that all models, particularly one with a very simple structure, can outperform Soil Moisture Active Passive (SMAP) measurements on a cross-fold analysis of the 1,000+ sites. This model has an average ubRMSE of 0.058 vs SMAPs 0.065 and an average R of 0.638 vs SMAPs 0.562. Not all ensembles are beneficial, with some architectures performing better with different training weights than with ensemble averaging. However, some ensembles capture more of the land surface characteristics than ensemble members. Lastly, although general improvements over SMAP are observed, there appears to be difficulty in consistently doing so in cropland regions with high clay and low sand content.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
855187