| packages |
[{'agreement': 'true', 'alternative_identifier': '', 'author': None, 'author_email': None, 'citation_title': 'Schutte, C., Van der Laan, M., & Van der Merwe, B. (2024). Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions. Journal of Hydroinformatics.', 'creator_user_id': '7ceb61ee-8458-44bd-9990-62b71be18dd4', 'data_classification': 'time series', 'data_collection_organization': '', 'data_structure_category': 'structured', 'dataset_language': '', 'dataset_license_url': '', 'did_author_or_contact_organization_collect_the_data': 'true', 'email': 'ceschutte34@gmail.com', 'id': '52d35ec0-4d95-49a3-b596-fa7e109dca75', 'isopen': False, 'keywords': 'GRU, LSTM, Rainfall-runoff modelling', 'license': 'Open (Creative Commons)', 'license_id': None, 'license_title': None, 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2023-07-27T15:17:57.228411', 'metadata_modified': '2024-02-16T13:54:08.702989', 'name': 'deep-learning-for-streamflow-prediction-project-data', 'notes': 'This dataset contains daily streamflow and weather data and the python code that was used to developed deep learning models for streamflow prediction. The data and code can be used to replicate the experiment on Google Drive using Google Colaboratory.\r\n\r\nStreamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning (DL) techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window (LBW) for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions. Including a change in activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data.', 'num_resources': 1, 'num_tags': 0, 'organization': {'id': '4e8a617a-148a-4440-adde-e503f9346fcd', 'name': 'university-of-pretoria', 'title': 'University of Pretoria', 'type': 'organization', 'description': 'The University of Pretoria (UP) is one of Africa’s top universities and the largest contact university in South Africa. ', 'image_url': '2022-05-24-062305.772520UP-logo24May2022.JPG', 'created': '2022-05-24T06:23:05.795364', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4e8a617a-148a-4440-adde-e503f9346fcd', 'private': False, 'publication_date': '2024-04-30', 'publisher': 'Water Research Commission', 'spatial': '{"type": "Polygon", "coordinates": [[[16.4699, -22.1265], [32.8931, -22.1265], [32.8931, -34.8212], [16.4699, -34.8212], [16.4699, -22.1265]]]}', 'state': 'active', 'title': 'Deep recurrent neural networks for daily streamflow prediction', 'type': 'metadata-form', 'uploader_estimation_of_extent_of_processing': 'access', 'url': None, 'version': None, 'vertical_extent_datum': '1800', 'wrc_project_number': 'C2020/2021-00440', 'wro_theme': 'hydrological_data_and_modelling', 'authors': [{'author_department': 'Plant and Soil Sciences', 'author_email': 'ceschutte34@gmail.com', 'author_name': 'Christiaan', 'author_organization': 'University of Pretoria', 'author_surname': 'Schutte', 'contact_same_as_author': 'true'}], 'contact_person': [{'contact_department': '', 'contact_email': '', 'contact_name': '', 'contact_orgnization': ''}, {'contact_department': '', 'contact_email': '', 'contact_name': '', 'contact_orgnization': ''}], 'data_reference_date': [{'data_reference_date_from': '1979-10-01', 'data_reference_date_to': '2002-02-22'}], 'extras': [{'key': 'cloud_path', 'value': 'Hydrological_Data_And_Modelling/Structured/Access/Time Series'}], 'minimum_maximum_extent': [{'maximum_vertical_extent': '2263', 'minimum_vertical_extent': '1336'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2024-02-13T15:39:11.287624', 'dataset_description': 'The folder contains the data needed to train GRU and LSTM models for streamflow prediction. See the ReadMe note inside the zipped folder for details on how to tun the experiment from Google Drive using Google Colab.\r\n\r\nDaily weather data, consisting of rainfall (mm), minimum and maximum temperature (̊C), and streamflow data (m3s-1) were used as input variables. The data included both in-situ measured data and gridded products. Daily weather data were obtained from three sources: 1) a weather station operated by the Agricultural Research Council (referred to as ARC in this study), 2) the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data (Funk et al. 2015) (https://www.chc.ucsb.edu/data/chirps), and 3) the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASAP) project (https://power.larc.nasa.gov/). Streamflow data were obtained from the DWS website (https://www.dws.gov.za/Hydrology/Verified/hymain.aspx) for stations B4H007 and B4H001 for Catchment A and Catchment B, respectively.', 'datastore_active': False, 'description': None, 'file_to_bigquery_table': 'False', 'format': 'zip', 'hash': '', 'id': 'd5e90237-e8f0-40e9-996e-2a19ea158669', 'is_bigquery_table': False, 'is_data_supplementary': 'False', 'last_modified': None, 'metadata_modified': '2024-02-13T15:39:13.933620', 'mimetype': None, 'mimetype_inner': None, 'name': 'Data_and_Code.zip', 'package_id': '52d35ec0-4d95-49a3-b596-fa7e109dca75', 'position': 0, 'resource_name': 'Project Data and Code', 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://storage.cloud.google.com/wrc_wro_datasets/hydrological_data_and_modelling/structured/access/time series/deep-learning-for-streamflow-prediction-project-data/Data_and_Code-8669.zip', 'url_type': 'link', 'zipped_file': 'False'}], 'tags': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'agreement': 'true', 'alternative_identifier': '', 'author': None, 'author_email': None, 'citation_title': '', 'creator_user_id': '7ceb61ee-8458-44bd-9990-62b71be18dd4', 'data_classification': 'static', 'data_collection_organization': '', 'data_structure_category': 'structured', 'dataset_language': '', 'dataset_license_url': 'Https://Creativecommons.Org/Licenses/By/4.0/', 'did_author_or_contact_organization_collect_the_data': 'true', 'email': 'vanderlaanm@arc.agric.za', 'id': '26ce7c99-69ad-43d1-a18a-337d6a92043e', 'isopen': False, 'keywords': 'HAMSA, SWAT, HAWQS, Soil Map', 'license': 'Open (Creative Commons)', 'license_id': None, 'license_title': None, 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2024-01-31T16:13:28.323655', 'metadata_modified': '2024-01-31T16:15:32.186014', 'name': 'swatcrossborder', 'notes': 'This WRC Funded Study Collated Multiple Geo-Spatial Datasets At A National Scale And Interpreted/Formatted The Data For Use As Baseline Input To Run The Soil And Water Assessment Tool (SWAT) In Any Catchment In SA. ArcSWAT Is A Graphical User Interface For SWAT And ArcGIS® Software Extension, Streamlining Access To Key Databases And Facilitating The Preparation Of Input Datasets. These Layers Can Also Be Used In QGIS And QSWAT. This dataset contains the swat input data for drainage areas that drain into South African rivers but fall outside the country boundary, such as in Botswana or Mozambique.', 'num_resources': 1, 'num_tags': 0, 'organization': {'id': '4e8a617a-148a-4440-adde-e503f9346fcd', 'name': 'university-of-pretoria', 'title': 'University of Pretoria', 'type': 'organization', 'description': 'The University of Pretoria (UP) is one of Africa’s top universities and the largest contact university in South Africa. ', 'image_url': '2022-05-24-062305.772520UP-logo24May2022.JPG', 'created': '2022-05-24T06:23:05.795364', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4e8a617a-148a-4440-adde-e503f9346fcd', 'private': False, 'publication_date': '2024-01-30', 'publisher': 'Water Research Commission', 'spatial': '{"type": "Polygon", "coordinates": [[[16.4699, -22.1265], [32.8931, -22.1265], [32.8931, -34.8212], [16.4699, -34.8212], [16.4699, -22.1265]]]}', 'state': 'active', 'title': 'SWAT Cross Border Data', 'type': 'metadata-form', 'uploader_estimation_of_extent_of_processing': 'access', 'url': None, 'version': None, 'vertical_extent_datum': '', 'wrc_project_number': 'WRC Project C2019-2020-00089', 'wro_theme': 'hydrological_data_and_modelling', 'authors': [{'author_department': 'Department Of Geography', 'author_email': 'LeRouxJJ@Ufs.Ac.Za', 'author_name': 'Jay', 'author_organization': 'University of the Free State', 'author_surname': 'Le Roux', 'contact_same_as_author': 'true'}], 'contact_person': [{'contact_department': '', 'contact_email': '', 'contact_name': '', 'contact_orgnization': ''}], 'data_reference_date': [{'data_reference_date_from': '', 'data_reference_date_to': ''}], 'extras': [{'key': 'cloud_path', 'value': 'Hydrological_Data_And_Modelling/Structured/Access/Static'}], 'minimum_maximum_extent': [{'maximum_vertical_extent': '3450', 'minimum_vertical_extent': '0'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2024-01-31T16:14:36.594117', 'dataset_description': 'This WRC Funded Study Collated Multiple Geo-Spatial Datasets At A National Scale And Interpreted/Formatted The Data For Use As Baseline Input To Run The Soil And Water Assessment Tool (SWAT) In Any Catchment In SA. ArcSWAT Is A Graphical User Interface For SWAT And ArcGIS® Software Extension, Streamlining Access To Key Databases And Facilitating The Preparation Of Input Datasets. These Layers Can Also Be Used In QGIS And QSWAT. 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From selected project data a publication was produced focusing on different methods to estimate irrigated maize evapotranspiration and calculate green and blue water footprints (Van der Laan, M., Jarmain, C., Bastidas-Obando, E., Annandale, J. G., Fessehazion, M., & Haarhoff, D. (2019). Are water footprints accurate enough to be useful? A case study for maize (Zea mays L.). Agricultural water management, 213, 512-520).\r\nAbstract\r\nThe application of water footprint accounting is mostly done at large scales, but the estimation of crop- and\r\nregion-specific water footprints for up-scaling is dependent on accurate and representative in-field measurements\r\nof evapotranspiration (ET), yield and irrigation. In a field trial we assessed the influence of maize (Zea\r\nmays L.) ET estimates using soil water balance accounting, remote sensing with satellite imagery (SEBAL model),\r\neddy covariance measurements and three crop models (SWB, CROPWAT, SAPWAT) on water footprint estimates.\r\nWe simultaneously assessed the influence of yield spatial variability as measured by a precision harvester.\r\nSeasonal ET estimations differed by as much as 15% for the different methods, and yield differed by as much as\r\n42%, representing the error which can be introduced as a result of point measurements. Using a combination of\r\nthe highest/lowest ET estimates and the 5th/95th percentile yield, water footprint values differed by as much as\r\n100%, ranging from 338-680m3 t−1. Applying spatially-linked SEBAL ET estimates and precision harvester yield\r\nat the 30×30m scale reduced the range of estimated water footprints to 493-663 m3 t -1, with an average of\r\n547m3 t-1. This was 15% higher than the water footprint estimated using average SEBAL ET and average yield\r\nfor the whole pivot (467m3 t -1). Any error introduced at this stage of water footprint accounting can be\r\ntransferred during up-scaling of the results. For example, based on the minimum and maximum estimated water\r\nfootprints, maize production was expected to consume between 4.4 and 8.3%, respectively, of the Orange River\r\n(South Africa’s largest river) flow during the season in that region. Biophysical scientists have the role of providing\r\nhigh quality data for accurate water consumption estimates. Thereafter, their application by various\r\nstakeholders should be done with caution.', 'num_resources': 92, 'num_tags': 0, 'organization': {'id': '4e8a617a-148a-4440-adde-e503f9346fcd', 'name': 'university-of-pretoria', 'title': 'University of Pretoria', 'type': 'organization', 'description': 'The University of Pretoria (UP) is one of Africa’s top universities and the largest contact university in South Africa. 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