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)

A&M vs. SMU

Background: High baseline galectin-3 levels are associated with increased risk for adverse cardiovascular outcomes in the general population, but determinants of changes in galectin-3 levels over time have not been established. Therefore, we aimed to identify determinants of (temporal) change in galectin-3 levels. Methods: Galectin-3 plasma levels were measured in a large community based cohort (PREVEND study) at 3 different time points: at baseline, after similar to 4 and similar to 9 years. The association of baseline clinical and biochemical factors and (temporal) changes in galectin-3 level was assessed using multivariable mixed-effects regression modeling. Results: In 4355 subjects, galectin-3 plasma levels were available at all time points (mean age: 48 +/- 12 years; 50% female). Median galectin-3 level at baseline was 10.7 [8.9-12.7] ng/mL which gradually increased to 11.5 [9.4-14.3] ng/mL after similar to 9 years. Using mixed-effects regression modeling, we first validated as independent determinants of baseline circulating galectin-3: eGFR (chi square (chi(2)): 210.27, p 30 mg/24 h and systolic blood pressure >170 mmHg were identified as significant determinants of dynamic increases in galectin-3 levels over time. These results implicate that treatment of high blood pressure might be effective to prevent increasing galectin-3 levels and its associated conditions. (C) 2016 Elsevier Ireland Ltd. All rights reserved

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

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

Under-canopy turbulence and root water uptake of a Tibetan meadow ecosystem modeled by Noah-MP

The Noah-MP land surface model adopts a multiparameterization framework to accommodate various alternative parameterizations for more than 10 physical processes. In this paper, the parameterizations implemented in Noah-MP associated with under-canopy turbulence and root water uptake are enhanced with: (i) an under-canopy turbulence scheme currently adopted by the Community Land Model (CLM), (ii) two vertical root distribution functions, i.e., an exponential and an asymptotic formulation, and (iii) three soil water stress functions (βt) controlling root water uptake, e.g., a soil water potential (ψ)-based function, a nonlinear soil moisture (θ)-based power function and an empirical threshold approach considering preferential uptake from the moist part of the soil column. A comprehensive data set of in situ micrometeorological observations and profile soil moisture/temperature measurements collected from an alpine meadow site in the northeastern Tibetan Plateau is utilized to assess the impact of the augmentations on the Noah-MP performance. The results indicate that (i) implementation of the CLM under-canopy turbulence scheme greatly resolves the overestimation of sensible heat flux and underestimation of soil temperature across the profile, (ii) both exponential and asymptotic vertical root distribution functions better represent the Tibetan conditions enabling a better representation of the measured soil moisture dynamics, and (iii) the ψ-based βt functions overestimate surface soil moisture, the default linear θ-based βt function underestimates latent heat flux during the dry-down, while both the nonlinear power function and empirical threshold approach simultaneously simulate well soil moisture, and latent and sensible heat fluxes. Additionally, the parameter uncertainty associated with soil water stress function and hydraulic parameterization is addressed

A 10-year (2009-2019) surface soil moisture dataset produced based on in situ measurements collected from the Tibet-Obs

The Tibet-Obs consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of the three networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks