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Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development and performance benchmarking. CAMELS (Catchment Attributes and Meteorological time series for Large Samples) datasets have been developed in several countries and regions around the world, providing valuable data sources and testbeds for hydrological analysis and new frontiers in data-driven hydrological modelling. Regarding the lack of samples from low-land, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both, gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. The dataset is enhanced with streamflow observations in 304 of those catchments. The spatially dense and full spatial coverage, supplying data for 3330 catchments, instead of only gauged catchments, together with the addition of simulation data from a distributed, process-based model enhance the applicability of such CAMELS data. This is especially relevant for the development of data-driven and hybrid physical informed modelling frameworks. We also provide quantities related to human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024). [ABSTRACT FROM AUTHOR]

Background Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). Results We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. Conclusions PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap. [ABSTRACT FROM AUTHOR]