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7 results for “SWAT model”
Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment
<p>The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019.</p> <p>Data deposit in preparation for article:</p> <p>Effect of spatial input data quality on the uncertainty of the<br> SWAT model, submitted 2022</p> <p>Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn<br> Uuemaa</p> <p>contact: alexander.kmoch@ut.ee</p>
Updated fruits and vegetable parameters for swat models
<p>Ensuring accurate crop yield simulations in ecohydrological models such as the Soil and Water Assessment Tool (SWAT) is crucial to improve our understanding of agricultural systems and productivity. This, in turn, can facilitate the development of more sustainable agricultural practices. In this study, we focused on validating the crop growth parameters of the SWAT model for 24 table food fruits and vegetables in Iowa, located in the western Corn Belt region of the United States.</p> <p>To estimate these parameters, we used five primary sources: a) existing parameters in the SWAT crop parameter database, b) alternative parameters in the Environmental Policy Integrated Climate (EPIC) and Agricultural Policy/Environmental eXtender (APEX) crop parameter databases, c) literature, d) PHU fraction for scheduling dates, and e) expert communication among modeling team members. Among the 24 crops tested, 15 initial parameter data sets were already available in the SWAT database, and the remaining crop types were added to this plant.dat file.</p>
Training Deep Learning Models to Estimate SWAT Parameters using Streamflow Observations
<p>This folder provides the simulation and observational data</p> <p>Simulation data using SWAT (1000 realz)<br> Train, Val, and Test splits (80/10/10)<br> Observational data for ARW (WY2000-2016)</p>
Simulations for SA and SC of SWAT+ model
<p>This repository contains the simulations run with SWATPlusR to perform the Sensitivity analysis and the Soft calibration for a SWAT+ model constructed for the Upper Tagus River basin.</p> <p> </p> <p>In this repository you will find:</p> <p>The raw simulations for the Sensitivity analysis --> SA_runs.rar</p> <p>The files created when extracting the data from the simulations for the Sensitivity analysis --> var_extract.zip</p> <p> </p> <p>The raw simulations for the Soft calibration:</p> <p>For each iteration, the file that contains the entire output from the 1000 simulations is the ronda_X_lsu.rds</p> <p>The file channels_ordered_all.rds contains the streamflow simulated for the evaluated subbasins for the three soft calibration iterations.</p> <p> </p> <p>More info and the code used to analyse this data can be found in the GitHub repository (https://github.com/alejandrosgz/https://github.com/alejandrosgz/HSJ_SA_SC_SWATPlus)</p> <p> </p>
SWAT-GL Demo Model Martelltal
<p>Here we provide an example model of SWAT-GL in the Martelltal with updated input files and the latest revisions. </p>
SWAT & MODFLOW Model Components
<p>The dataset contains the input and output data of two different model components. The components are based on the hydrological models SWAT and MODFLOW and integrated in the MINT framework. </p>
Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments
<p>This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [<em>NSE</em>] > 0.5), whereas SWAT generally yielded unsatisfactory results (<em>NSE</em> < 0.5) and tended to overestimate daily streamflow by up to 27% (<em>PBIAS</em>). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.</p> <p>This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results.</p> <p>The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.</p>
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Allen Brain Atlas
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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.