Resultados de investigación

This dataset includes data corresponding to the paper "Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications" with DOI: 10.3390/app10010202

Data set associated with the published chapter entitled: La sostenibilidad ambiental en la gestión pública y empresarial: ¿desarrollo o equilibrio sostenible? García Fernández, Gregorio. (2022). Capítulo 14: La sostenibilidad ambiental en la gestión pública y empresarial: ¿desarrollo o equilibrio sostenible? En: Portillo Navarro, María José, Baixauli Soler, J. Samuel (Eds.). Ecoinnovación desde una perspectiva del sector público. Thomson Reuters Aranzadi. Pp. 307-327.

Physicochemical soil properties of the long-term case study LT1 of Diverfarming H2020 project for diversified vegetables in southeast Spain. Includes data of the research article "Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems" (https://doi.org/10.3390/agriculture11050445)

Stormwater management model (SWMM) software has recently become a modeling tool for the simulation of intermittent water supply systems. However, SWMM is not capable of accurately simulating the air behavior in the pipe-filling phase, missing therefore a relevant factor during pipe pressurization. This work proposes the integration of a conventional accumulator model in the existing SWMM hydraulic model to overcome this gap. SWMM source code was modified to calculate the air piezometric head inside the pipe based on the system boundary conditions, and the air piezometric head was incorporated in the SWMM flow rate pressure component. Experimental data were collected during the rapid filling of a pipe system for three possible configurations that are likely to occur in intermittent water supply pipe systems: no air release, small air release, and large air release. Results show that the improved SWMM better describes the effect of the air behavior using the extended transport (EXTRAN) surcharge method when compared to the original SWMM. Results also show that the SLOT method with predefined slot width is not suitable for this purpose; thus, further research is needed to assess if an adjusted slot width could provide better results.

Data set associated with the published article entitled: Implications of the Phytoremediation of Heavy Metal Contamination of Soils and Wild Plants in the Industrial Area of Haina, Dominican Republic.

Data set associated with the published article entitled: In vitro lead tolerance and accumulation in three Chrysanthemum cultivars for phytoremediation purposes with ornamental plants.

Dataset of pomegranate tree (Punica granatum L. ‘Wonderful’) image times series. The pictures were collected by means of Raspberry Pi cameras with OV5647 sensor (5 MP, f2.9). Sensors were installed on fixed platforms for continuous measurement with zenithal orientation at a distance of approximately 1 metre from the canopy. Images were captured daily at 9 a.m. (GMT+2) from July to mid-October in 2021 and 2022. The resolution of the images is 640x480 pixels.

This dataset includes data corresponding to the paper "Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature" with DOI: 10.1016/j.compag.2021.106319

The paper proposes a novel methodology to locate and quantify entrapped air pockets created during pipe-filling events often found in intermittent water supply systems. Different filling conditions were tested in an experimental pipe with a high point. Measurements were taken and video recordings were carried out to assess air pocket volumes for different air release conditions at the downstream end of the pipe. The stochastic nature of air pocket creation resulted in varying air volumes. A new numerical model capable of simulating the air pocket creation, dragging and entrainment has been proposed. The new model, AirSWMM, was implemented as an extension of the Stormwater Management Model (SWMM) with stochasticity of air pocket formation reproduced by simulations with different air entrainment rates. The obtained numerical results show that the proposed model, even though based on a single-phase one-dimensional flow, can accurately locate and approximately quantify the entrapped air pocket volumes

VERSION HISTORY: -On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data. - On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit)., The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2