Bridging hydrology and pedology to predict physical and hydraulic properties of soils in Lower Congo

C

Iron ore mining areas and their reclamation in Minas Gerais State, Brazil: impacts on soil physical properties.

Plant cover acts to maintain the balance between soil chemical, physical, and biological attributes, as well as superfcial soil protection. The aim of this study was to evaluate the impacts of iron ore mining and their reclamation on soil physical properties and soil visual quality in Fort Lauderdale Municipal Park and Serra do Curral Municipal Park, Iron Quadrangle (Quadrilátero Ferrífero), Minas Gerais State, Brazil. The evaluated areas varied in relation to the post-mining condition, natural revegetation (NR), an area with gully erosion (GA) and area under eucalyptus revegetation (ER) and native vegetation cover, rupestrian feld (RF), and seasonal semi-deciduous forest (NF). The main soil physical attributes evaluated were: soil organic matter (SOM), geometric mean diameter (GMD), weighted mean diameter (WMD), bulk density (Bd), air capacity (ACb), plant-available water capacity (AWC), relative feld capacity (RFC), and visual soil quality assessment. In addition to the impacts on the landscape, with removal of vegetation and soil cover, iron ore mining process impacts soil physical quality measured through porosity and aggregation properties and therefore could impact ecosystems services. Areas of iron post-mining that are not restored can develop gully erosion. NR shows high erosion risk inferred through aggregation indicators (GMD=3.84 mm; MWD=3.04 mm), despite similar soil organic matter content and higher plant-available water and air (NR [AWC=0.102 m3 m−3; ACb=0.328 m3 m−3], NF [AWC=0.062 m3 m−3; ACb=0.202 m3 m−3]) compared with NF (GMD=4.77 mm; MWD=4.56 mm). ER had similar soil structure stability compared to NF as well most of the porosity indicators, which is associated with the higher soil organic matter. Soil visual assessment alone was not able to characterize the soil physical quality, mainly in the post-mining areas, because it was designed for agricultural soils.Publicado online em 9 set. 2020

Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin

Water retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426-km(2) subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R-2, mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR-PTF and ML-PTFs, the R-2 of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN-PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application. Highlights Developed machine learning and multiple linear regression pedotransfer functions (PTFs). The Saxton & Rawls PTFs are not recommended for use in the Zambezi River Basin. PTFs were functionally evaluated through use of estimated soil hydraulic properties in AquaCrop. More accurate PTFs have better functional performance, although differences are small