J. van der Kwast

Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain)

Accurate quantification of the amount and spatial variation of evapotranspiration is important in a wide range of disciplines. Remote sensing based surface energy balance models have been developed to estimate turbulent surface energy fluxes at different scales. The objective of this study is to evaluate the Surface Energy Balance System (SEBS) model on a landscape scale, using tower-based flux measurements at different land cover units during an overpass of the ASTER sensor over the SPARC 2004 experimental site in Barrax (Spain). A sensitivity analysis has been performed in order to investigate to which variable the sensible heat flux is most sensitive. Taking into account their estimation errors, the aerodynamic parameters ( hc, z0M andd0) can cause large deviations in the modelling of sensible heat flux. The effect of replacement of empirical derivation of these aerodynamic parameters in the model by field estimates or literature values is investigated by testing two scenarios: the Empirical Scenario in which empirical equations are used to derive aerodynamic parameters and the Field Scenario in which values from field measurements or literature are used to replace the empirical calculations of the Empirical Scenario. In the case of a homogeneous land cover in the footprints of the measurements, the Field Scenario only resulted in a small improvement, compared to the Empirical Scenario. The Field Scenario can even worsen the result in the case of heterogeneous footprints, by creating sharp borders related to the land cover map. In both scenarios modelled fluxes correspond Correspondence to: J. van der Kwast (hans.vanderkwast@vito.be) better with flux measurements over uniform land cover compared to cases where different land covers are mixed in the measurement footprint. Furthermore SEBS underestimates sensible heat flux especially over dry and sparsely vegetated areas, which is common in single-source models.

Modelling landscape dynamics with Python

A new tool for construction of models is presented that allows earth scientists without specialist knowledge in programming to convert theories to numerical computer models simulating landscape change through time. This tool, referred to as the PCRaster Python library, consists of: (1) the standard Python programming language, which is a generic, interpreted scripting language, supporting object oriented programming; (2) a large set of spatial and temporal functions on raster maps that are embedded in the Python language as an extension; (3) a framework provided as a Python class to construct and run iterative temporal models and to calculate error propagation with Monte Carlo simulation; and (4) visualization routines to display spatio‐temporal data read and written by this framework. Python is a high‐level programming language, and users of the tool do not have to be specialist computer programmers. Users of the PCRaster Python library can take advantage of several other Python libraries, such as extensions for matrix algebra and for modelling in three spatial dimensions.

Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system

Abstract Advances in earth observation (EO) and spatially distributed hydrological modelling provide an opportunity to improve modelling of controlled water systems. In a controlled water system human interference is high, which may lead to incorrect parameterisation in the model calibration phase. This paper analyses whether assimilation of EO actual evapotranspiration (ETa) data can improve discharge simulation with a spatially distributed hydrological model of a controlled water system. The EO ETa estimates are in the form of eight-day ETa composite maps derived from Terra/MODIS images using the ITA-MyWater algorithm. This algorithm is based on the surface energy balance method and is calibrated for this research for a low-lying reclamation area with a heavily controlled water system: the Rijnland area in the Netherlands. Data assimilation (DA) with the particle filter method is applied to assimilate the ETa maps into a spatially distributed hydrological model. The hydrological model and DA framework are applied using the open source software SIMGRO and PCRaster-Python respectively. The analysis is done for a period between July and October 2013 in which a high discharge peak followed a long dry-spell. The assimilation of EO ETa resulted in local differences in modelled ETa compared to simulation without data assimilation, while the area average ETa remained almost the same. The modelled cumulative discharge graphs, with and without DA, showed distinctive differences with the simulation, with DA better matching the measured cumulative discharge. The bias of simulated cumulative discharge to the observed data reduced from 14% to 4% when using DA of EO ETa. These results showed that assimilating EO ETa may not only be effective in the more common applications of soil moisture and crop-growth modelling, but also for improving discharge modelling of controlled water systems.