Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km2). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.</p

The Role of Orthographic and Semantic Learning in Word Reading and Reading Comprehension

We tested the theoretically driven hypotheses that children’s orthographic and semantic learning are associated with their word reading and reading comprehension skills, even when orthographic and semantic knowledge are taken into account. A sample of 139 English-speaking Grade 3 children completed a learning task in which they read stories about new inventions. Then, they were tested on their learning of the spelling and meaning of the inventions (i.e., orthographic and semantic learning, respectively). Word reading and reading comprehension were assessed with standardised tasks, and orthographic and semantic knowledge were assessed with choice tasks targeting the spelling and meaning of existing words. The results of our structural equation modeling indicated that orthographic learning predicted word reading directly and reading comprehension indirectly via word reading. We also found that semantic learning predicted reading comprehension directly. These findings support integration of the self-teaching hypothesis and the lexical quality hypothesis

Detection and attribution of flood trends in Mediterranean basins

Floods have strong impacts in the Mediterranean region and there are concerns about a possible increase in their intensity due to climate change. In this study, a large database of 171 basins located in southern France with daily discharge data with a median record length of 45 years is considered to analyze flood trends and their drivers. In addition to discharge data, outputs of precipitation, temperature, evapotranspiration from the SAFRAN reanalysis and soil moisture computed with the ISBA land surface model are also analyzed. The evolution of land cover in these basins is analyzed using the CORINE database. The trends in floods above the 95th and 99th percentiles are detected by the Mann-Kendall test and quantile regression techniques. The results show that despite the increase in extreme precipitation reported by previous studies, there is no general tendency towards more severe floods. Only for a few basins is the intensity of the most extreme floods showing significant upward trends. On the contrary, most trends are towards fewer annual flood occurrences above both the 95th and 99th percentiles for the majority of basins. The decrease in soil moisture seems to be an important driver for these trends, since in most basins increased temperature and evapotranspiration associated with a precipitation decrease are leading to a reduction in soil moisture. These results imply that the observed increase in the vulnerability to these flood events in recent decades is mostly caused by human factors such as increased urbanization and population growth rather than climatic factors