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 (h_c, z_0M and d₀) 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 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.

Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960 m) Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) land surface temperature image products (MOD11_L2, MOD11A1 and MOD11A2) to resolutions of 60, 90, 120, 240 and 480 m; and (2) to propose an improved version of the DisTrad method for downscaling the MODIS/Terra land surface temperature products over urban areas. The proposed improved DisTrad is based on a better parameterization of the original DisTrad residuals in urban areas. The improved resampling technique is based on a regression relationship between the residuals of the temperature estimation and the impervious percentage index. Validation of the improved DisTrad, the original DisTrad, and the uniformly disaggregated MODIS land surface temperature images (UniTrad) are performed by comparative analysis with a time-coincident Landsat 7 ETM+ thermal image. Statistical results indicate that the improved DisTrad method shows a higher correlation (R² = 0.48) with the observed temperatures than the original DisTrad (R² = 0.43) and a lower mean absolute error (MAE = 1.88 °C) than the original DisTrad (MAE = 2.07 °C). It is concluded that the improved DisTrad method has a stronger capability to downscale land surface temperatures in urban areas than the original DisTrad.

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.

Kombolcha, a city in Ethiopia, exemplifies the challenges and problems of the sub-Saharan countries where industrialization is growing fast but monitoring resources are poor and information on pollution unknown. This study monitored metals Cr, Cu, Zn, and Pb concentrations in five factories’ effluents, and in the effluent mixing zones of two rivers receiving discharges during the rainy seasons of 2013 and 2014. The results indicate that median concentrations of Cr in the tannery effluents and Zn in the steel processing effluents were as high as 26,600 and 155,750 µg/L, respectively, much exceeding both the USEPA and Ethiopian emission guidelines. Cu concentrations were low in all effluents. Pb concentrations were high in the tannery effluent, but did not exceed emission guidelines. As expected, no metal emission guidelines were exceeded for the brewery, textile and meat processing effluents. Median Cr and Zn concentrations in the Leyole river in the effluent mixing zones downstream of the tannery and steel processing plant increased by factors of 52 (2660 compared with 51 µg Cr/L) and 5 (520 compared with 110 µg Zn/L), respectively, compared with stations further upstream. This poses substantial ecological risks downstream. Comparison with emission guidelines indicates poor environmental management by industries and regulating institutions. Despite appropriate legislation, no clear measures have yet been taken to control industrial discharges, with apparent mismatch between environmental enforcement and investment policies. Effluent management, treatment technologies and operational capacity of environmental institutions were identified as key improvement areas to adopt progressive sustainable development.