Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

In today’s changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earth’s terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalisation of any physically based model. A Global Sensitivity Analysis (GSA) was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs) for the model inputs, when examining sensitivity of key quantities simulated by SimSphere at different output times, were examined. The development of topographic model input parameters (e.g., slope, aspect, and elevation) were derived within a Geographic Information System (GIS) before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, fractional vegetation cover, vegetation height and surface moisture availability in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values of the output variance decomposition, but it did not seem to change the relative importance of each input parameter. Sensitivity Analysis (SA) results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling and educational tool. Furthermore, this study is very timely given on-going efforts towards the development of operational products based on the synergy of SimSphere with Earth Observation (EO) data. In this context, results also provide additional support for the potential applicability of the assimilation of spatial analysis data derived from GIS and EO data into an accurate modelling framework

Quantifying the prediction accuracy of a 1-D SVAT model at a range of ecosystems in the USA and Australia: evidence towards its use as a tool to study Earth's system interactions

This paper describes the validation of the SimSphere SVAT (Soil–Vegetation–Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (Rg), net radiation (Rnet), latent heat (LE), sensible heat (H), air temperature at 1.3 m (Tair 1.3 m) and air temperature at 50 m (Tair 50 m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation. Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the Rg, Rnet, Tair 1.3 m and Tair 50 m parameters. The simulated Rg parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ~ 24 % of the observed fluxes was reported for the Rnet parameter for all days of study (RMSD = 58.69 W m−2). A systematic underestimation of Rg and Rnet (mean bias error (MBE) = −19.48 and −16.46 W m−2) was also found. Simulations for the Tair 1.3 m and Tair 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 °C (within ~ 15 and ~ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m−2, ~ 34 and ~ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash–Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations. To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products

Appraising the capability of a land biosphere model as a tool in modelling land surface interactions: results from its validation at selected European ecosystems

In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere's performance in predicting Net Radiation (<i>R</i>_net), Latent Heat (LE), Sensible Heat (<i>H</i>) and Air Temperature (<i>T</i>_air) at 1.3 and 50 m was examined. Model simulations were validated by ground-based measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation. <br><br> Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared, predicted <i>H</i> fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36 W m⁻². LE fluxes and <i>R</i>_net also agreed well with the in-situ data with RSMDs of 62.75 and 64.65 W m⁻² respectively. A good agreement between modelled and measured LE and <i>H</i> fluxes was found, especially for smoothed daily flux trends. For both <i>T</i>_air 1.3 m and <i>T</i>_air 50 m a mean RMSD of 4.14 and 3.54 °C was reported respectively. <br><br> This work presents the first all-inclusive evaluation of SimSphere, particularly so in a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model's structure and its correspondence to the real world system. Findings also further establish the model's capability as a useful teaching and research tool in modelling Earth's land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products &amp; Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Operational tools and applications of EO satellite data to retrieve surface fluxes in semi-arid countries

The overall objective of the thesis is to develop and evaluate useful tools and applications of Earth Observation (EO) satellite data to estimate surface fluxes in semiarid countries. The first specific objective (Chapter 4) is to assess the performance and robustness of the triangle method, a remote sensing (RS) based method to retrieve and map land evapotranspiration (ET). Emphasis is put on the estimation of soil heat flux (G), which represents an important sink of energy in these regions. Estimating accurately G is still very problematic, because of its dependence on both vegetation cover fraction and soil physical properties. To that aim, the partial goals are: - Proposal of a new parameterisation scheme for G, based on a linear relationship with the evaporative fraction (EF) - Assessment of the ET retrieval triangle method with the new Gparameterisation As a second specific objective (Chapter 5), the triangle method was compared with predictions supplied by a land surface model (JULES) with the purpose of illustrating how these two different approaches can be combined to improve the estimations of surface fluxes. The third objective is to demonstrate the potential of RS-based information for actual operational applications related to the monitoring of surface fluxes in semi-arid countries (Chapter 6). The first one is the application of the triangle method to obtain maps of actual ET in western Africa. The second one is the application of the same method to analyse the annual evolution of surface energy balance (SEB) components and to study drought climatology over a 12-year period in the Segura river basin in South-East Spain. The third is the use of sea surface temperature (SST) coupled to an energy balance model of a water body to characterise the thermal behaviour of coastal lagoon (Mar Menor) located in South-East Spain.Universidad Politécnica de Cartagen