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37 results for “Process-based Model”
Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based Vegetation Photosynthesis Model (VPM), and a process-based Soil Canopy Observation of Photochemistry and Energy flux (SCOPE) model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data
<p>gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations.</p> <p>It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).</p>
Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models"
<p><strong>Data for the publication "Learning from mistakes - Assessing the performance and uncertainty in process-based models"</strong></p> <p>The corresponding python code can be found at <a href="http://github.com/MoritzFeigl/Learning-from-mistakes">github.com/MoritzFeigl/Learning-from-mistakes</a>.</p> <p>This dataset contains data of hydrological and meteorological observations of the Fortress Ski Area (Alberta, Canada) for August 8-26, 2019. It consists of input and output files for the HFLUX models calibration period (C) and the validation periods (V, V3). The input files containes additional data used in the learning from mistakes workflow. The meteorological data and part of the hydrological data were provided by John Pomeroy and the University of Saskatchewan’s Cold Water Laboratory.</p>
Intermediate data belonging to "Process-based climate change assessment for European winds using EURO-CORDEX and global models"
<p>This dataset contains the intermediate results of Wohland (2022) that are needed to redo the analysis und produce the figures. It allows to bypass those steps that rely on access to the supercomputers at the German Climate Computing Centre (DKRZ). When using this data in academic work, please reference</p> <blockquote> <p>Jan Wohland, Process-based climate change assessment for European winds using EURO-CORDEX and global models, Environmental Research Letters (provisionally accepted on 28/11/2022), 2022</p> </blockquote> <p><strong>Using this data to reproduce results</strong></p> <p>The data can be used together with the code provided in https://github.com/jwohland/kliwist_modelchain</p> <p>In the above mentioned github repository, there is a `run_all.py` script that repeats the analysis presented in Wohland (2022). After downloading and extracting this data, you can ignore the steps under "calculations", and begin with "plots".</p> <p><strong>Underlying data</strong></p> <p>The dataset draws on output from the CMIP5, CMIP6 and EURO-CORDEX initiatives. I thank the climate modeling groups for making their data openly available. In particular, I acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. I also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). I also acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6.</p> <p><strong>Funding</strong></p> <p>This work is part of the project "The influence of climate change on wind energy site assessments – KliWiSt" funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).</p> <p><strong>References to raw data journal articles</strong></p> <blockquote> <p>Jacob, D. <em>et al.</em> EURO-CORDEX: new high-resolution climate change projections for European impact research. <em>Reg Environ Change</em> <strong>14</strong>, 563–578 (2014).</p> </blockquote> <blockquote> <p>Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the Experiment Design. <em>Bull. Amer. Meteor. Soc.</em> <strong>93</strong>, 485–498 (2012).</p> </blockquote> <blockquote> <p>Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> <strong>109</strong>, 117–161 (2011).</p> </blockquote>
Simulations from four process-based ecosystem models describing primary productivity in a tallgrass prairie long-term irrigation experiment
<p class="MsoNormal"><span>To demonstrate current capabilities in modeling herbaceous ecosystems, we selected four different process-based models that vary in their representation of community change from no community representation to vegetation demographic models. These models were used to simulate a long-term irrigation experiment at a US tallgrass prairie (Konza Prairie Biological Station) following a standardized simulation protocol. Specifically, we were interested in how model output under a monotonic increase in water availability matched up to experimental findings of (1) herbaceous plant community change and (2) aboveground net primary productivity before and after the plant community change. The results of this simulation are included here.</span></p>
Data from: Regional paleoclimates and local consequences: Integrating GIS analysis of diachronic settlement patterns and process-based agroecosystem modeling of potential agricultural productivity in Provence (France)
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Data from: Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize
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Confronting assumptions about prey selection by lunge-feeding whales using a process-based model
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Simulations from four process-based ecosystem models describing primary productivity in a tallgrass prairie long-term irrigation experiment
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Data and code from: Live birth in lizards: A process-based model for the roles of temperature, behavior, and life-history
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Data from: Comparing process-based and constraint-based approaches for modeling macroecological patterns
Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.
Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets
<p>This repository contains the model 3D-CMCC-FEM v5.6 executable (Testolin et al.2023), model inputs and model outputs in the folder RUN_BASILICATA; scripts to prepare model inputs and perform model outputs post-processing in SCRIPTS; remote-sensing based data and forcing in DATA; tables and post-processed files in OUTPUT; figures in FIGURE, related to the manuscript entitled “Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets”</p>
Data from: Comparing process-based and constraint-based approaches for modeling macroecological patterns
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Supporting data to A process-based model to track water pollutant generation at high resolution and its pathway to discharge
<p>This dataset includes parameters for point sources (industrial production, residential and commercial consumption) and activity data for artificial drainage activities. Please see the publication for more details.</p>
Data supporting the findings of "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale"
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.