Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

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

Continental scale downscaling of AWRA-L analysed soil moisture using random forest regression

The Australian Water Resource Assessment Landscape (AWRA-L) model as used by the Bureau of Meteorology (BoM) provides daily continental scale soil moisture (SM) estimates (among other landscape water variables) at ~5-km resolution. At such a coarse scale these data cannot represent the high spatiotemporal variability of SM across heterogeneous land surfaces. Downscaling of coarse SM products based on machine learning (ML) has become increasingly popular due to its robust predictions and potential for large-scale applications. As a first step towards high-resolution daily Australia-wide SM estimation, a downscaling framework was developed to generate monthly SM with 500-m spatial resolution using analysed SM from AWRA-L and multisource geospatial predictors in random forest (RF) regression. Candidate predictors include digital elevation model (DEM), soil properties from the Australian soil and landscape grids, and several retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS). Ten experiments were conducted to decide the best combination of predictors. In the chosen model, DEM and available water capacity (AWC) were consistently identified as the most important predictors based on the ranking of variable importance. The downscaled SM shows greatly enhanced spatial details at the local scale while maintaining consistent patterns with AWRA-L analysis at the continental scale. Validations against in-situ measurement networks using Pearson correlation coefficient (R) show that there is very little difference in the performance between the downscaled and AWRA-L SM. Average R values for the downscaled SM against CosmOz, OzFlux and OzNet were 0.87, 0.68 and 0.75, respectively, while the original AWRA-L SM average R were 0.86, 0.68 and 0.76, respectively. Furthermore, the time series comparison based on a wetness unit shows that the downscaled SM can well catch up the fluctuations of in-situ SM. In general, this study explores the potential of ML approach for the SM downscaling applications at the continental scale. It could be a promising direction to exploit the modelling capability of integrating multisource geospatial data including satellite retrievals, land surface models (LSM) and interpolated ground observation data. Future directions should concentrate on integrating this approach into an operational framework with a daily frequency. Exploration of the relationships between SM and auxiliaries under difference scales would be essential, in order to better understand the dominant physical controls on spatial variability of SM.This research was undertaken while supported by the Australian National University (ANU) University Research Scholarship and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and ANU Digital Agriculture Supplementary Scholarship through the Centre for Entrepreneurial Agri-Technology (CEAT). This research was supported with funds from the University of Sydney (USYD) and Grains Research and Development Corporation (GRDC) project SoilWaterNow

On the uncertainty induced by pedotransfer functions in terrestrial biosphere modeling

Hydrological, ecohydrological, and terrestrial biosphere models depend on pedotransfer functions for computing soil hydraulic parameters based on easily measurable variables, such as soil textural and physical properties. Several pedotransfer functions have been derived in the last few decades, providing divergent estimates of soil hydraulic parameters. In this study, we quantify how uncertainties embedded in using different pedotransfer functions propagate to ecosystem dynamics, including simulated hydrological fluxes and vegetation response to water availability. Using a state-of-the-art ecohydrological model applied at 79 sites worldwide, we show that uncertainties related to pedotransfer functions can affect both hydrological and vegetation dynamics. Uncertainties in evapotranspiration, plant productivity, and vegetation structure, quantified as leaf area, are in the order of ∼10% at annual time scales. Runoff and groundwater recharge uncertainties are one order of magnitude larger. All uncertainties are largely amplified when small-scale topography is taken into account in a distributed domain, especially for water-limited ecosystems with low permeability soils. Overall, pedotransfer function related uncertainties for a given soil type are higher than uncertainties across soil types in both hydrological and ecosystem dynamics. The magnitude of uncertainties is climate-dependent but not soil type-dependent. Evapotranspiration, vegetation structure, and plant productivity uncertainties are higher in water-limited semiarid climates, whereas groundwater recharge uncertainties are higher in climates where potential evapotranspiration is comparable to precipitation

Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain

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

Multi-product characterization of surface soil moisture drydowns in the UK

The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in-situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple UK soil moisture products, including satellite-merged (i.e. TCM), in-situ (i.e. COSMOS-UK), hydrological model (i.e. G2G), statistical model (i.e. SMUK) and land surface model (LSM) (i.e. CHESS) data. The drydown decay time scale (τ) for all gridded products are computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of τ differ (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyse the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales

Modelling the influence of widespread afforestation on UK hydrology

With increasing atmospheric CO2, the hydrological system is moving towards more frequent and intense hydroclimatic extremes. With the greater risk this poses to society, we need solutions that reduce atmospheric CO2 and mitigate water-related hazards. Afforestation, mooted to reduce atmospheric CO2 and mitigate flood risk, is being proposed internationally at greater temporal and spatial scales than ever witnessed before. Previous work has assessed the hydrological consequences of woodland planting at relatively small scales (< 10 km2) or at global scales with low process and spatial resolution. There is a clear need for evidence at countrywide scales on whether afforestation will achieve its intended goals. The work here seeks to determine the influence of widespread afforestation on UK hydrology. The UK plans to annually plant 30 000 hectares of trees to reach its Net Zero goals. This work uses land surface modelling at a higher complexity than is often undertaken when understanding woodland hydrology. Land surface models include a relevant set of Earth system processes, which is critical when drawing conclusions about woodland hydrology. The final research piece uniquely couples a land surface model and convection-permitting atmospheric model to simulate the hydrometeorological consequences of UK widespread afforestation. In this thesis, afforestation location has a minimal impact on terrestrial hydrology compared to afforestation extent. However, in a land-atmosphere model configuration, woodland along Great Britain’s west coastline increases surface roughness, producing heavier rainfall. Median streamflow reduces by 2.8% ± 1.0 (1 s.d.) for a ten-percentage point increase in catchment broadleaf woodland but there is no consistent reduction of extreme floods. Afforestation minimally impacts hydrological processes compared to changes in precipitation, temperature, and CO2. More arid catchments show greater streamflow sensitivity to woodland expansion potentially increasing the likelihood of drought formation with afforestation. Work here provides a critical step forward in our understanding of afforestation impact on hydrology and the utility of land surface models in answering policy-relevant questions