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Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution. [ABSTRACT FROM AUTHOR]

Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections. [ABSTRACT FROM AUTHOR]

Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters. [ABSTRACT FROM AUTHOR]

Soil moisture is an important component of the Earth system and plays a key role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several satellite-based coarse spatial resolution soil moisture datasets have been produced and widely used for various applications in climate science, hydrology, ecosystem research and agriculture. Owing to the strong demand for soil moisture data with high spatial resolution for regional applications, much effort has recently been devoted to the generation of high spatial resolution soil moisture data from either high-resolution satellite observations or by downscaling existing coarse-resolution satellite-based soil moisture datasets. In addition, land surface models provide an alternative way to obtain consistent high-resolution soil moisture information when forced with high-resolution inputs. The aim of this study is to create and evaluate high-resolution soil moisture products derived from multiple sources including satellite observations and land surface model simulations. The JULES-CHESS simulated soil moisture and satellite-based soil moisture datasets including SMAP L3E, SMAP L4, SMOS L4, Sentinel 1, ASCAT, and Sentinel 1/SMAP combined products were first validated against observed soil moisture from COSMOS-UK, a network of in-situ cosmic-ray based sensors. Second, an approach based on triple collocation was applied to compare these satellite products in the absence of a known reference dataset. Third, a combined soil moisture product was generated to integrate the better-performing soil moisture estimates based on triple collocation error estimation and a least-squares merging scheme. From further evaluation, it is found that the merged soil moisture integrates the characteristics of model simulation and satellite observations and particularly improves the limited temporal variability of the JULES-CHESS simulation. Therefore, we conclude that the triple collocation merging scheme is a simple and reliable way to combine satellite-based soil moisture products with outputs from the JULES-CHESS simulation for estimating model-data fused high-resolution soil moisture for the British mainland. • Evaluation of satellite and JULES-CHESS soil moisture against 38 COSMOS-UK sites. • SMAP L3E and JULES-CHESS show better performance compared to other products. • All products show high errors over surface with organic soils. • Blended soil moisture integrates both model and satellite characteristics. [ABSTRACT FROM AUTHOR]