Data underlying the publication: Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data

This dataset includes the input data, Python scripts, and Pastas model output for the scientific manuscript "Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data". The manuscript is currently under review. The data covers the years 2016, 2017, and 2018. We refer to the readme file included in the dataset for further details

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2016-04 / 2017-04 [version 1]

Measurements from 2016-04 to 2017-04

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2018-04 / 2019-04

The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and is measurements are on-going. In ‘additional_datasets.txt’ we describe additional datasets which are freely available for the Raam catchment (elevation, soil physical, land use, groundwater level and meteorological data)

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2017-04 / 2018-04

The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and the measurements are on-going

Regional soil moisture monitoring network in the Raam catchment in the Netherlands - 2016-04 / 2017-04 (corrected)

Note: The original dataset (https://data.4tu.nl/repository/uuid:dc364e97-d44a-403f-82a7-121902deeb56) contains errors. Some loggers were not correctly set to cope with daylight saving time differences. Therefore, some data are incorrectly shifted by an hour. This shift is corrected in this dataset. Original text: The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and the measurements are on-going

Replication Dataset: Analyzing natural bed-level dynamics to mitigate the morphological impact of river interventions

The river bed level in low-land rivers like the Rhine branches in the Netherlands changes continuously on various spatial and temporal scales. We use biweekly (2005-2020) multibeam bed-level measurements of the river Bovenrijn/Waal to study the morphological changes on multiple scales using a wavelet transform. The dataset contains the unfiltered and the filtered results that were presented in the article. The data is aggregated in the interactive atlas to show the bed-level changes in time and as function of the discharge.</p

Replication Dataset: Analyzing natural bed-level dynamics to mitigate the morphological impact of river interventions

The river bed level in low-land rivers like the Rhine branches in the Netherlands changes continuously on various spatial and temporal scales. We use biweekly (2005-2020) multibeam bed-level measurements of the river Bovenrijn/Waal to study the morphological changes on multiple scales using a wavelet transform. The dataset contains the unfiltered and the filtered results that were presented in the article. The data is aggregated in the interactive atlas to show the bed-level changes in time and as function of the discharge.</p

Regional soil moisture monitoring network in the Raam catchment in the Netherlands

The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and the measurements are on-going

Regional soil moisture monitoring network in the Raam catchment in the Netherlands

The Raam soil moisture measurement network dataset contains soil moisture and soil temperature measurements for 15 locations in the Raam, which is a 223-km2 river catchment in the southeast of the Netherlands. The network monitors soil moisture in the unsaturated zone for different soil textures and land covers present in the area, and it covers the topographic gradient of the region. At each location we installed Decagon 5TM sensors at depths of 5 cm, 10 cm, 20 cm, 40 cm and 80 cm. The logging time interval is set on 15 minutes. The Raam network is operational since April 2016 and the measurements are on-going