COSMOS-UK: National soil moisture and hydrometeorology data for empowering UK environmental science research

The COSMOS-UK observation network has been providing field scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high temporal resolution data in near-real time. Each site utilises a cosmic-ray neutron sensor, which counts fast epithermal neutrons at the land surface. These measurements are used to derive field scale near-surface soil water content, 35 which can provide unique insight for science, industry, and agriculture by filling a scale gap between localised point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological 40 forecasting and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/37702a54-b7a4-40ff-b62ed14b161b69cahttps://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185 (Stanley et al., 2021).


Table 1: Site information. Standard Average Annual Rainfall (SAAR) is provided by the Flood Estimation Handbook (FEH)
90 catchment descriptor SAAR6190 as described in Bayliss (1999)

Site name
Start (  The selection of sites within the network has aimed to provide an appropriate spatial coverage for improving understanding of UK soil moisture conditions, including representation of key land cover and soil types. All UK regions are represented, though there are more sites in the south and east of the UK to adequately capture the greater soil moisture variability in these areas. 100 Installation of sites in less represented regions is in consideration but is dependent on the availability of resources.
Specific site locations have been further determined by practical considerations such as long-term permission and reasonable access for instrument installation and maintenance, and mobile phone network coverage. Where possible, site selection has aimed to exploit opportunities for COSMOS-UK data to support independent, existing research projects, e.g. data assimilation for forecasting and prediction; validation of remote sensing data; and support of other monitoring programmes and activities. 105 Similarly, site selection has aimed to create partnerships with farmers and support agricultural research. Some site characteristics can limit their suitability for CRNS soil moisture measurement, such as proximity to open water or shallow or perched groundwater (such features should not be present within the CRNS measurement footprint), and highly variable topography. Sites have therefore been installed in non-mountainous and largely flat locations with no regular irrigation or close proximity to significant water bodies. 110

Site data acquisition
Instrumentation at COSMOS-UK sites is largely standardised (Fig. 2), however differences have arisen for the following reasons. 8  When instrument performance was reviewed resulting in subsequent installations utilising different, higherperformance sensors (e.g. for improved sensor accuracy). 115  Where a site has been located in an area which is expected to experience a significant period of snow cover, the monitoring equipment includes additional sensors for measurements of snow.
 Where a site has been located within a forest and requires measurements from a tower structure above the canopy of mature vegetation.
These site differences are detailed in Table 3. For further information regarding individual instruments, a detailed summary is 120 provided in the COSMOS-UK User Guide (Boorman et al., 2020).  Sensor calibration coefficients are stored on the CR3000 for measurements such as soil heat flux (G, W m -2 ) and the four 135 components of net radiation (RN, W m -2 ). Equipment across the network is promptly replaced when faults are detected, and instruments are tested and re-calibrated on an annual basis under a maintenance contract with the suppliers of the field instrumentation, Campbell Scientific Ltd. A full record of sensor exchanges is maintained by UKCEH.

Soil data
Each COSMOS-UK site utilises a moderated CRS2000/B CRNS (Hydroinnova LLC, Albuquerque, New Mexico, USA) which 140 counts fast epithermal neutrons at the land surface. The sites at Chimney Meadows, Sheepdrove and Wytham Woods were Some sites have previously utilised installed with a bare and/or moderated CRS1000/B, and Waddesdon was installed with only a moderated CRS1000/B (Hydroinnova LLC, Albuquerque, New Mexico, USA) . All bare CRNSs have subsequently been removed. Wytham Woods was decommissioned in 2016, and in February 2020 CRS2000/B sensors were installed adjacent to the remaining CRS1000/B instruments. The neutron counts from these sensors are used to derive 145 average field scale volumetric water content (VWC, %) of the near-surface soil layer (see Sect. 3.1 for details). Each site includes point scale soil moisture sensors, which estimate VWC via Time Domain Transmissometry (TDT). These TDT sensors estimate point scale soil moisture by measuring the time taken for an electromagnetic wave to travel along the sensor's closed circuit; this transmission decreases in speed with soil permittivity (Blonquist et al., 2005). Each site includes either two (deployment prior to March 2016) or ten buried ACC-SEN-SDI point soil moisture sensors (TDTs) (Acclima Inc., Idaho, 150 USA) to measure small-area soil VWC (%) at defined depths (listed in Table 4 TDT1  TDT2  TDT3  TDT4  TDT5  TDT6  TDT7  TDT8  TDT9

Hydrometeorological data 165
COSMOS-UK sites include a Pluvio 2 (L) digital weighing rain gauge (OTT HydroMet, Kempten, Germany) installed with an aperture height of 1 m above the soil surface. These rain gauges measure precipitation intensity and amount (mm) at one 1minute resolution. Sites were identified as being not particularly exposed and therefore Pluvio wind shields were not installed.
Incoming and outgoing short-and long-wave radiation (W m -2 ) are measured at each site using an NR01 four-component net   (Vaisala Corporation, Helsinki, Finland). From this, pressure corrected to sea level is derived. Air temperature (°C) and relative humidity (%) are measured at every site using either an HC2(A-)S3 (Rotronic, Bassersdorf, Switzerland) or HMP155(A) sensor (Vaisala Corporation, Helsinki, Finland). Air temperature and relative humidity are measured at the standard height of 2 m. Wind speed and direction are measured using either a 2-dimensional WindSonic at a measurement height of 2.2 m or 3-175 dimensional WindMaster anemometer (Gill Instruments Limited, Lymington, UK) at a measurement height of 2.6 m.

Non-standard sites
COSMOS-UK sites located in dense forest or woodland (Alice Holt, Harwood Forest and Wytham Woods) were designed with certain meteorological sensors installed above the canopy, on pre-existing flux monitoring towers. Wind measurements, barometric pressure, relative humidity, air temperature, precipitation, and the components of net radiation are measured above 180 the canopy. The measurement height of these variables ranges from approximately 23-33 m. Precipitation is captured by a funnel above the canopy and fed via a tube to the Pluvio 2 (L) rain gauge located at ground level. Forest sites do not accurately measure rainfall intensity due to the lag time in precipitation captured above canopy and recorded in the rain gauge below.
Precipitation data are corrected for the smaller aperture area of the funnel relative to that of the Pluvio 2 (L).
Across the COSMOS-UK network, eight site locations were identified in areas likely to experience a significant period of 185 snow cover over the winter period. These sites (Glensaugh, Easter Bush, Gisburn Forest, Plynlimon, Sourhope, Moor House, Cwm Garw and Cochno) were installed with two additional sensors: an SR50A snow depth sensor (Campbell Scientific Ltd., Logan, Utah, USA) measuring small area snow depth (mm); and a buried SnowFox CRNS (Hydroinnova LLC, Albuquerque, New Mexico, USA) measuring neutron counts which can be used to derive snow water equivalent (Desilets, 2017).
Tadham Moor is located on the Somerset Levels, an area that can experience inundation during high rainfall. The 190 COSMOS-UK site was therefore adapted to withstand any significant floodwater. For this reason, the digital weighing rain gauge has an aperture height of approximately 1.7 m, and the CRNS is installed horizontally at a height of approximately 1.1 m rather than vertically. This non-standard installation enables an assessment of the CRNS technology in a very high soil moisture environment.
During COSMOS-UK network maintenance in February 2020 an SBS500 tipping bucket rain gauge (Environmental 195 Measurements Limited, North Shields, UK) was added to three sites (Chimney Meadows, Sheepdrove and Waddesdon), providing an additional precipitation (mm) reference against which the performance of the Pluvio 2 (L) rain gauges can be evaluated. The SBS500 tipping bucket rain gauge (TBR) was chosen for its improved aerodynamic characteristics and reduction in turbulence and under-catch (Colli et al., 2018;Strangeways, 2004).

Soil sampling and lab analysis for site calibration 200
An in situ soil sampling procedure adapted from Franz (2012) and Zreda et al. (2012) has been completed at each COSMOS-UK site following installation. The results from the sampling are used to determine site-specific soil properties for CRNS calibration: field average soil moisture and dry bulk density, lattice and bound water, and organic matter. Once the CRNS count data have been corrected for atmospheric pressure (Desilets, 2017;Evans et al., 2016), humidity (Evans et al., 2016;Rosolem et al., 2013) and an empirical background neutron intensity factor (Blake et al., 2020)(adapted from Evans et 205 al., 2016), the calibration data are used to derive N0 on the day of calibration (details in Sect. 3.1). Soil samples for determination of VWC and dry bulk density are were taken at 18 representative locations centred on the CRNS: at compass bearings of 0, 60, 120, 180, 240 and 300 degrees and at 5, 25 and 75 m radial distance at each of these compass bearings (  The field soil samples were returned to the laboratory for analysis. VWC and dry bulk density were determined for the 90 volumetric samples using oven drying (~36 hours at 105 °C). Following analysis, a ~2 g sub-sample was taken from each 225 sample and aggregated to form a composite sample for lattice and bound water and organic matter determination. The three additional soil samples from the field were air dried (on the lab bench or in the oven at 30 °C) for around three days. The additional samples, along with the composite, were then crushed to pass a ~0.4 mm sieve and subsequently air dried at 105 °C for ~36 hours. Soil organic matter was then estimated for a ~3 g air dried sub-sample (with 6 replicates per additional sample, i.e. 24 sub-samples) using loss on ignition at 400 °C for 16 hours in the furnace (following Nelson and Sommers, 1996). 230 Following cooling in a desiccator and weighing, the sub-samples were then returned to the furnace to estimate lattice and bound water by loss on ignition at 1000 °C for 4 hours (following Pansu and Gautheyrou, 2006). For use in the CRNS calibration calculation, soil organic carbon was estimated as 50 % of soil organic matter (Nelson and Sommers, 1996). Pansu and Gautheyrou (2006) note that loss on ignition removes organic matter at 300-500 °C and lattice and bound water at 350-1000 °C. The procedure outlined above therefore follows the 400 °C temperature recommendation by Nelson and Sommers 235 (1996), which removes organic matter but causes minimal dehydroxylation of clay minerals. The CRNS calibration procedure uses the mean soil organic carbon and mean lattice and bound water from the 24 sub-samples along with the mean dry bulk density from the 90 volumetric samples. The field average reference VWC for the day of calibration is then calculated as a radial and vertical weighted mean following Köhli et al. (2015). Planned work includes obtaining site bulk density using this weighting function. . The soil properties and soil moisture results for calibrating each from each soil calibrationsite are 240 available in Table 5.
Secondary samples have been collectedRepeat calibrations using secondary samples have been conducted at two COSMOS-UK sites to explore the accuracy of the derived VWC obtained on a particular day using this methodology. There was < 0.03 cm 3 cm -3 difference in VWC between the soil moisture determined from the these second samples calibration and the corresponding daily VWC value derived using the site's initial first calibration. Considering the estimated errors in soil 245 sampling and (to a lesser extent) laboratory procedures, the difference in calibrations is considered to be within the uncertainty of the reference soil moisture determined from secondary sampling and the predicted VWC from the CRNS and its original calibration. data. The results from each soil calibration are available in Table 5. Additional repeat calibrations are planned across the network to help further analyse the current methodologies and assess sensor performance over time.

250
In Eq. (1), Ncorr are the corrected counts, is the reference lattice and bound water, SOC is the reference soil organic carbon, ρbd is the reference bulk density and ρw is the water density equal to 1 g cm -3 . , SOC and ρbd are determined on the calibration day by field and laboratory analysis (Evans et al., 2016;Franz, 2012;Franz et al., 2013;Zreda et al., 2012). Ncorr is obtained by aggregating raw nNeutron counts from each site are aggregated to a 60-minute interval and corrected correcting for 265 atmospheric pressure (Desilets, 2017;Evans et al., 2016), and humidity (Evans et al., 2016;Rosolem et al., 2013) and background neutron intensity variations (adapted from (Evans et al., 2016)) using in situ measurements. The atmospheric pressure correction uses instantaneous barometric attenuation lengths (Desilets and Zreda, 2003) calculated for COSMOS-UK sites according to crnslab.org/util/intensity.php and the correction uses a fixed reference pressure value of 1000 hPa. A subsequent correction is applied for background neutron intensity variation (Desilets, 2017;Blake et al., in review), using The 270 background neutron intensity correction uses the publically available Jungfraujoch (JUNG) data (nmdb.eu/station/jung/) fromprovided by the Physikalisches Institut's, University of Bern, Jungfraujoch (JUNG) neutron detector in Switzerland (nmdb.eu/station/jung/). Normalised count rates from JUNG data are retrieved and used in sub-daily calculations to produce near-real time COSMOS-UK datasets; the period of record is subsequently updated for any changes to JUNG data on an annual basis. Where data are unavailable from the JUNG detector the period is infilled with appropriately scaled values from alternate 275 monitors: another counter at Jungfraujoch (JUNG1), Newark in the USA (NEWK) provided by the University of Delaware Department of Physics and Astronomy and the Bartol Research Institute, or Apatity in Russia (APTY). When choosing the most suitable neutron monitors for COSMOS-UK data, these monitors were identified as well-maintained with high levels of data completeness. The geomagnetic cut-off rigidity of the available monitors' locations was also considered when identifying suitable monitors. Normalised count rates are not greatly affected by cut-off rigidity except for during significant space weather 280 events, when magnetic field disturbances may result in a change to a location's cut-off rigidity. A comparison between JUNG and monitors with cut-off rigidities similar to COSMOS-UK sites presented good agreement between the normalised counts Formatted  COSMOS-UK uses the site-specific N0 method (Desilets et al., 2010) for deriving water content from a site's corrected neutron 285 count data, where N0 is the site-specific neutron counting rate over dry soil under reference atmospheric pressure and solar activity conditions. Alternative methods are described in Baatz et al. (2014), Bogena et al. (2015) and Iwema et al. (2015). A site-specific N0 value is calculated by rearranging Eq. (1) for N0 and substitutingusing the average neutron counts on the day of calibration for N, together with reference soil moisture for VWC., reference soil lattice and bound water and reference soil organic carbon as determined for the calibration day by field and laboratory analyses (Franz, 2012;Franz et al., 2013;Zreda 290 et al., 2012;Evans et al., 2016). The calculation also relies on parameters determined by the relationship between corrected neutron counts and soil moisture as defined for a basic silica soil (Desilets et al., 2010). VWC is then derived from the corrected counts using N0. The corrected counts and N0 can then be input into Eq. (1) to produce VWCs. These data are subsequently constrained to the physical range of 0-100 % soil water content by determining values of Nmax and Nmin respectively, the maximum and minimum physically admissible neutron count value for each site. Figure 4 shows an example of the calibration 295 curve for the Redhill site, located in South East England. Once complete, this process produces the hourly CRNS VWC dataset. In a subsequent process, hourly corrected neutron counts are averaged to a daily mean and undergo the same calculations to produce the daily CRNS VWC soil moisture dataset. A minimum of 20 hourly values in a day is set as the requirement to produce a daily soil moisture value. An additional version of the soil moisture dataset is calculated, in which daily CRNS VWC has been adjusted for snow events using site measurements of albedo. 310

Formatted: Subscript
An in-lab cross-comparison was performed on the majority of CRNSs prior to field deployment. Cross-calibration of deployed CRNSs was also carried out at six COSMOS-UK sites; data were captured from two adjacent CRNSs for a period of several months to establish a reliable relationship between their counts using a linear regression model. Point soil moisture and precipitation data at each COSMOS-UK site provide important ancillary information for assessing the potential accuracy of the CRNS VWC data. Figure 5 shows each of the processing stages for deriving soil water content from 315 neutron counts for the Cochno site in Scotland, alongside soil moisture measured by the 10 buried point sensors and precipitation. This figure clearly shows that daily CRNS VWC data closely resemble the soil moisture dynamics measured by the point sensors, and the response of both VWC measurements to precipitation events. Some data uncertainty exists for sites with high levels of soil moisture (Blake et al., 2020).
Some sites may have a higher CRNS VWC measurement uncertainty. TheseFor example high moisture sites often 320 correspondsites with extensive soil organic matter accumulation (e.g. carbon-dense peatlands) or mature woodlands where CRNS VWC methods might need to be further refined to account for biomass, plant roots, litter-layer thickness and intercepted water (Andreasen et al., 2017;Baatz et al., 2015;Heidbüchel et al., 2016;Rivera Villarreyes et al., 2011). The contrast of CRNS VWC measurements between sites can be seen in Fig. 6, which displays all data for the period of record as a normalised curve for each site. This figure demonstrates the importance of identifying and understanding localised soil properties, and 325 shows how sites in close proximity and experiencing broadly similar weather patterns can exhibit vastly different ranges and extremes in VWC.

Soil moisture measurement area and depth
The CRNS VWC value is an average soil moisture measurement (%) across an estimated, variable footprint of radius up to 345 200 m, and estimated, variable measurement depth of between approximately 0.1 and 0.8 m (following Köhli et al., 2015).
Measurement area depends on local soil moisture, humidity and land cover (Köhli et al., 2015b), whilst penetration depth depends on soil moisture as well as lattice water and soil organic matter water equivalent (Zreda et al., 2008;Franz et al., 2012;Zreda et al., 2012). The greater the actual soil water content, the smaller the CRNS measurement area and shallower the penetrative depth. The measurement area of the CRNS was initially believed to have a radius of approximately 300 m (Zreda 350 et al., 2008); however Köhli et al., (2015) report that 50 % of measured neutrons originated within 50 m of the CRNS, and the footprint radius extended to only 240 m in arid climates. The penetration depth of the measurement is greatest near the CRNS and decreases with distance from the sensor; this varying depth across the footprint is provided as 'D86', the depth at which 86 % of the measured neutron counts are estimated to have originated at a given distance (Zreda et al., 2008;Franz et al., 2013). In the COSMOS-UK dataset, D86 is provided at distances of 1, 5, 25, 75, 150 and 200 m from the CRNS.

Data processing and quality procedures
Raw data collected at each COSMOS-UK site, comprising the measured variables described above as well as additional diagnostic data from sensors (e.g. internal humidity of the CRNS), are telemetered to UKCEH and stored in an Oracle relational database (Oracle, 2013). When new values are derived following the application of corrections, calibrations and quality tests, these derived data are stored in separate, secondary, tables. These secondary datasets are those that are published. 370 Data quality assurance (QA) and quality control (QC) are applied to specific variables in the raw data. QC is conducted in two stages: 1. Automated processing is applied to raw data to provide a quality assured dataset. Data which fail the tests are flagged and are not written to secondary datasets. These automated tests include pre-processing for known errors and subsequent QC processes for detecting additional erroneous data. These processes are explained below. 375 2. Regular manual inspection of raw, diagnostic and processed data is performed using a variety of automated summary plots and reports. Clearly erroneous data that have passed the automated QC tests are flagged and omitted from the secondary dataset.
Automated processing tasks assess the raw data and create a flagged dataset based on the test results. This enables tracking of data removal and ensures raw data are not lost or overwritten. Raw data are passed through multiple independent QC tests 380 (Table 6). Each test assigns a uniqueUnique flag values are assigned to any raw data values where the datawhich fails a specific QC test (Table 6) All derived datasets are obtained using the quality checked 30 minute data. Planned future work includes the development of a tertiary dataset comprising quality processed and gap-filled data.

Derived data
In addition to the COSMOS-UK observed soil and hydrometeorological data, the network provides derived datasets including potential evaporation (PE), albedo, snow days, and snow water equivalent (SWE).
PE has been derived from each site's solar radiation, soil heat flux, air temperature, humidity, and wind speed data using the 395 Penman-Monteith method as described by the Food and Agriculture Organization of the United Nations (FAO) (Allen et al., 1998) (Fig. 8). 30 minute and dDaily PE data for all COSMOS-UK sites are provided in this dataset.
Snow days have been identified using albedo measurements and SWE has been determined using the albedo and neutron count data available from the CRNS at each COSMOS-UK site. Neutron counts from both the CRNS and SnowFox sensor are sensitive to all sources of water in the environment, allowing them to be used to estimate the SWE held in a snow pack. First 400 the albedo is used to determine the presence or absence of snow cover and then, if present, the reduction in neutron count rate from an estimated snow snow-free value is used to approximate the SWE, following the method of Desilets (2017). This dataset includes CRNS SWE. Alternative mMethods for estimating SWE are available from Wallbank et al. (2020b) and discussed in more detail in Wallbank et al. (2020a).
Available derived data are listed in Sect. 4. 405

Additional available data
Additional information can be derived from the data provided by COSMOS-UK sites. As part of ongoing and planned evolution of the network, the additional data described in this section are not yet included in the published data.
Existing PE data will be complemented by a new derived dataset, which estimates actual evapotranspiration (ET) as the residual 415 term from measurements of net radiation, soil heat flux and the sensible heat flux derived from sonic anemometer measurements. Modelled energy fluxes, such as latent and sensible heat, have been calculated by utilising the 20 Hz wind measurements recorded at the majority of COSMOS-UK sites (Crowhurst et al., 2019). This provides a network-wide modelled actual ET dataset for the UK.
In addition to the measurements mentioned previously, COSMOS-UK sites also capture photographs. Sites include a camera 420 for monitoring phenology, a 'PhenoCam', with two hemispheric lenses facing north and south (Fig. 9). Each COSMOS-UK site sends five photographs per day, which capture the full extent of the COSMOS-UK site and surrounding area, thereby providing additional information on local phenology and cloud cover. These PhenoCam images can be used to confirm when site conditions have changed, for example when the land cover has been modified (e.g. ploughing, mowing, grazing, harvesting) or there has been heavy snowfall. PhenoCam photos from COSMOS-UK sites are also currently being analysed to 425 produce a greenness dataset. Using site-specific image masks, RGB (red, green, blue) data can be extracted from each image to determine the greenness of the land cover at each site (Wingate et al., 2015). In 2020 the network's first gauge board was installed at the Cwm Garw site in Wales; as gauge boards are installed across the network, new vegetation height and snow depth data will become available via the PhenoCam images. Gauge boards indicate height above ground level (cm) against which vegetation height and snow depth can be estimated via PhenoCam images. Further gauge boards are planned at sites 430 across the network.

Data availability
The "Daily and sub-daily hydrometeorological and soil data  [COSMOS-UK]" time series dataset is the most recent COSMOS-UK dataset at the date of publication. The dataset is published by, and available for download from, the EIDC at https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69cahttps://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-440 598da03a1185 (Stanley et al., 2021).  Table 7 comprises the measured and derived variables, units and recording intervaltemporal resolution of data available in these files. File 1 contains measured and derived variables at 30 minute resolution and file 2 comprises the QC flags for the data in file 1. File 3 comprises the derived variables available at hourly resolution and file 4 contains derived data at daily 450 resolution. Data availability for individual variables and sites varies throughout the dataset due to sensor faults, planned preventative 460 maintenance, and disruptions to data collection. Overall data completeness for this period for available variables is 95.56% (see a summary in Fig. 10) (Stanley et al., 2021). Missing values due to technical faults and failed QC calculations are recorded as -9999.
COSMOS-UK has been designed as a long-term monitoring network and Ffurther data will also be made available via the EIDC. The dataset is superseded annually, with the inclusion of one additional year of COSMOS-UK data for all available 465 sites. Data are provisional and subject to change with the release of each new version in line with developments to the science, instrumentation, data processing, quality control, and data gap-filling protocols. Data are supplied with supporting information and a data licence that outlines the terms of use to data users.

Data applications
Observational data from the COSMOS-UK network have been used for a variety of purposes. They have significant potential to empower a range of existing and novel scientific applications. Descriptions of some uses are included in this section. The 475 main and immediate applications for COSMOS-UK observational data are for use in hydrological and land-surface models and for validating remote sensing data.
COSMOS-UK measurements cover a range of environmental characteristics and this can be exploited for model driving data and further development of models, which are used for scaling up and forecasting soil moisture at the national scale. Field scale soil moisture measurements from a variety of land covers have been used to investigate the accuracy and reliability of 480 LSMs. Comparison of COSMOS-UK soil moisture measurements with outputs from LSMs allows for investigation into those models' ability to represent soil moisture dynamics and underlying physical processes (Cooper et al., 2020a). For example, data assimilation techniques have been used to adjust soil physics parameters (via pedo-transfer functions), thereby allowing the JULES model to more closely produce the observed range of soil moisture values (Cooper et al., 2020b). This demonstrates the value in using in situ COSMOS-UK data to drive models for increased performance. Additional potential exists in using 485 these larger area data across a variety of land covers to explore interactions and dynamics in infiltration, run-off (Dimitrova-Petrova et al., 2020) and interception . Improved understanding of these processes could lead to more accurate and reliable modelling of, and thus improved forecasting for, a range of hydrological phenomena. For instance the JULES model, used as the land-surface scheme in UK Met Office forecasts (Best et al., 2011), is run at a minimum scale of 1 km. The parameterisation of this model can be improved in response to these soil moisture data (Cooper et al., 2020b), 490 which can then be used with UK scale meteorological data  to deliver a national scale soil moisture product.
Using land-atmosphere modelling together with COSMOS-UK soil moisture and modelled ET data, along with measured ET where available, can empower further investigation into soil moisture dynamics and biosphere-atmosphere fluxes. These combined data can provide greater understanding of land-atmosphere processes, for example of feedback events during periods 495 of drying soils and extreme air temperatures (Dirmeyer et al., 2021) and storm initiation (Taylor et al., 2012). Use of these data can also help estimate landscape average precipitation, as described in Franz et al. (2020).
COSMOS-UK field scale soil moisture is also proving particularly useful for ground-truthing remote sensing soil moisture data. For this application, the value of COSMOS-UK data largely resides in the footprint of the CRNS. The field scale soil moisture data prove to be a radical improvement on point soil measurements alone, as the larger footprint more closely 500 represents the resolution of satellite products, whilst averaging across smaller-scale soil heterogeneity. COSMOS-UK data can therefore help validate and improve existing products (Beale et al., 2020;Pinnington et al., 2020;Quinn et al., 2020) for obtaining better estimates of UK soil moisture data at higher spatial resolution . Similar networks across the globe, for example in the US, India and China, have also been exploited for such research (Montzka et al., 2017;Upadhyaya et al., 2021;Zhu et al., 2019). COSMOS-UK soil moisture can be used together with PhenoCam data to further investigate 505 remote sensing analysis in vegetation growth, crop senescence, snow events, surface ponding, and land cover change.
With a vision to develop a dynamic near-real time UK soil moisture map, there is potential for COSMOS-UK data to influence wider fields. Scaled up near-real time COSMOS-UK data either through using models, remote sensing, or both could inform water-regulators such as the Environment Agency on the state of UK soil moisture. Direct evidence of drought and flooding events induced, or impacted, by soil moisture is increasingly needed to inform decisions at national scale. Similarly, these data 510 could help inform UK wildfire prediction and ecological applications via simulations of soil moisture, air temperature, precipitation, and vegetation information (Albertson et al., 2009). Additionally, with an understanding of the links between soil moisture and plant productivity, COSMOS-UK data can be used to monitor the need for irrigation (Ragab et al., 2017), thereby improving our predictions of crop yield for the UK. Furthermore, understanding soil moisture at identified landslip sites could help in the development of Landslide Early Warning systems, for example using the Hollin Hill COSMOS-UK site 515 in North Yorkshire (Bliss et al., 2020). At site scale, soil moisture data from individual COSMOS-UK sites have proven valuable when paired with gas flux data provided by field scale methodologies such as eddy covariance (Cowan et al., 2018(Cowan et al., , 2020. Here the high temporal, spatially-integrated soil moisture data can be used to better refine gap-filling methods, particularly for emissions of the powerful GHG nitrous oxide, which responds strongly to changes in soil aerobicity. As all of the major GHGs (CO2, CH4, N2O), and many secondary GHGs and other sources of air pollution (CO, NO, NO2) generated 520 by soil microbial activity, are heavily influenced by soil moisture (Cowan et al., 2018;Davidson et al., 2000;Oertel et al., 2016;Van Den Pol-van Dasselaar et al., 1998), the COSMOS-UK network will provide the ability to better refine UK scale emission inventories in the future as UK scale soil moisture models are improved.
COSMOS-UK data could also provide insight into alternative scientific research, such as the relationship between soil moisture and pest behaviour (Hertl et al., 2001); the impact of soil moisture on local infrastructure (Pritchard et al., 2013); investigation 525 of ground level cosmic ray events (Flückiger et al., 2005); and meteorological data with respect to bacterial infection seasonality (Djennad et al., 2019).

Conclusions
The COSMOS-UK network is the world's most spatially dense national network of cosmic-ray neutron sensors for observing near-surface field scale soil water dynamics. Field scale soil moisture and hydrometeorological data are available from a 530 diverse range of sites located across the UK, with the earliest sites providing data since 2013. The COSMOS-UK dataset is a unique and growing resource that has already captured soil water dynamics across a wide range of climatic conditions, including extreme events such as the extended cold wave, heatwave and agricultural drought the UK experienced during 2018.
As the length of the data record continues to grow, COSMOS-UK will provide an unprecedented resource for national scale environmental monitoring. Data from the COSMOS-UK network are of significant national and international relevance 535 empowering applications including the validation of remotely sensed data products, the interpretation of biogeochemical flux observations, and the calibration and testing of LSMs. Significant opportunity exists for new applications in support of water resources, weather prediction and space sciences, and biodiversity and environmental change.