The soil moisture status near the land surface is a key determinant of vegetation productivity. The critical soil moisture content determines the transition from an energy-limited to a water-limited evapotranspiration regime. This study quantifies the critical soil moisture content by comparison of in situ soil moisture profile measurements of the Raam and Twente networks in the Netherlands, with two satellite-derived vegetation indices (near-infrared reflectance of terrestrial vegetation, NIRv, and vegetation optical depth, VOD) during the 2018 summer drought. The critical soil moisture content is obtained through a piece-wise linear correlation of the NIRv and VOD anomalies with soil moisture on different depths of the profile. This non-linear relation reflects the observation that negative soil moisture anomalies develop weeks before the first reduction in vegetation indices: 2–3 weeks in this case. Furthermore, the inferred critical soil moisture content was found to increase with observation depth, and this relationship is shown to be linear and distinctive per area, reflecting the tendency of roots to take up water from deeper layers when drought progresses. The relations of non-stressed towards water-stressed vegetation conditions on distinct depths are derived using remote sensing, enabling the parameterization of reduced evapotranspiration and its effect on gross primary productivity in models to study the impact of a drought on the carbon cycle.

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( ${\sigma }^0$ ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on ${\sigma }^0$ and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb ${\sigma }^0$ were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 ${\sigma }^0$ observations appear to be affected by frozen conditions below an air temperature of 1 ${}^{\circ }\mathrm{C}$ , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than $1.8$ $\mathrm{m}$ $\mathrm{m}$ of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest ${\sigma }^0$ observations. By spatially averaging the ${\sigma }^0$ observations, the Sentinel-1 radiometric uncertainty improves from $0.85$ dB for a surface area of $0.25$ $\mathrm{h}\mathrm{a}$ to $0.30$ dB for 10 $\mathrm{h}\mathrm{a}$ for the VV polarization and from $0.89$ dB to $0.36$ dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the ${\sigma }^0$ observations are averaged. Deviations in ${\sigma }^0$ were combined with the ${\sigma }^0$ sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from $0.01$ ${\mathrm{m}}^3$ ${\mathrm{m}}^{-3}$ up to $0.17$ ${\mathrm{m}}^3$ ${\mathrm{m}}^{-3}$ for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the ${\sigma }^0$ disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself.

We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km²), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km²). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m³ m⁻³. The first set of measurements has been retrieved for the period 5 April 2016–4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.

The Twente region in the east of the Netherlands has a network with 20 soil monitoring stations that has been utilized for validation of the Soil Moisture Active/Passive (SMAP) passive-only soil moisture products. Over the period from April 2015 until December 2018, seven stations covered by the SMAP reference pixels have fairly complete data records. Spatially distributed soil moisture simulations with the Dutch National Hydrological Model have been utilized for the development of upscaling functions to translate the spatial mean of point measurements to the domain of the SMAP reference pixels. The native and upscaled spatial soil moisture means computed using the in situ measurements have been adopted as references to assess the performance of the SMAP (i) Single Channel Algorithm at Horizontal Polarization (SCA-H), (ii) Single Channel Algorithm at Vertical Polarization (SCA-V), and (iii) Dual Channel Algorithm (DCA) soil moisture estimates. In the case of the Twente network it was found that the SCA-V SMAP soil moisture observations collected in the afternoon had the best agreement with the native spatial mean, leading to an unbiased root mean squared error (uRMSE) of 0.059 m³ m⁻³, whereas for the upscaled in situ references primarily larger biases were found. These error levels are larger than the mission's target accuracy of 0.04 m³ m⁻³, which can be attributed to large over- and under-estimation errors (>0.08 m³ m⁻³), in particular at the end of dry spells and during freezing, respectively. The strong vertical dielectric gradients associated with rapid soil freezing and wetting cause the disparity in soil depth characterized by SMAP and in situ that leads to the large mismatches. Once filtered for frozen conditions and antecedent rainfall, the uRMSE improves to 0.043 m³ m⁻³.