ABSTRACT Hydrosedimentological modeling is a useful tool to predict the water dynamic in a basin and for water resources management. This study aimed to i) evaluate the ability of Soil and Water Assessment Tool (SWAT) to model sediment load and continuous monthly streamflow in the Mortes River Basin (MRB) in Southeastern Brazil; ii) estimate the sediment yield spatially distributed by sub-basins; iii) estimate the sediment load export to the Funil Hydroelectric Power Plant reservoir (FHPP), located in the MRB outlet. For the sensitivity analysis, calibration, and uncertainty analysis of the model, a semi-automatic calibration in SWAT-CUP version 5.1.6 software with the “Sequential Uncertainty Fitting” algorithm was used. To evaluate the ability of SWAT to reproduce the continuous MRB monthly streamflow and sediment load, statistical indexes, and graphical analyses were used to compare the simulated and observed data. For the sediment evaluation, a spatial and temporal comparison of sediment yield maps was used as well as the sediment yield observed in sub-basins, aiming to identify the areas with a more significant contribution to the sediment generation in the basin. The results demonstrated that SWAT performed satisfactorily in simulating both monthly sediment load and streamflow. For discharge calibration, 99 % of the measured data were bracketed by the 95 % prediction uncertainty (95PPU), and for validation, 97 % of the data were bracketed by the 95PPU, which indicates proper bracketing of the measured data within model prediction uncertainty. Uncertainty analysis indicated that 95PPU could capture 78 % of the sediment loads measured during the calibration and 72 % of the measured data during the validation period at MRB. The hydrologic response unit with pasture and Argissolos (Ultisols), Neossolos Litólicos (Entisols), and Cambissolos (Inceptisols) combined with undulated relief were the main areas responsible for the highest sediment contributions. The sediment load delivered to the reservoir from its filling 2002 to 2015 was estimated in 6,682,704 m3 (16,706,761 Mg) (density of 2.5-Mg m-3) which value corresponded to 2.6 % of storage capacity (water plus sediment) in 14 years. These results are strategic since to become feasible identifying priority areas for soil and water conservation practices as well as useful information for water resources planning and management in the studied basin.

ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.

ABSTRACT: The Mantiqueira Range region is one of the most important headwaters in southeastern Brazil. In this context, the relationship between pedology and hydrology has been debated and analyzed in recent years, contributing to the creation of a multidisciplinary science call hydropedology. Therefore, the aim of this study was to evaluate the hydropedological properties of a mountainous Clayey Humic Dystrudept in the Mantiqueira Range region, in the state of Minas Gerais, Brazil, comparing two land-uses, native forest and pasture. The hydraulic conductivity results demonstrated that native forest (MFO) has a strong influence on this parameter, offering conditions for movement of water in the soil that are more adequate in this profile than in pasture. These results were supported by pore size distribution analyses which showed that soil from native forest has a greater amount of macropores than soil from pasture as well as greater connectivity between the macropores. In general, the MFO site had greater S index values than the micro-catchment taken from pasture, offering favorable physical conditions for the formation of preferential flowpaths in the soil profile and, therefore, better conditions for groundwater recharge. Soil erosion and water quality results confirmed the importance of native forest areas in the interaction between interception of the direct impact of intense precipitation on the soil surface, and hydropedological attributes, such as saturated hydraulic conductivity and porosity.

ABSTRACT: Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km2) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil. Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R2 values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.

RESUMO Mapas de solos existentes (dados legados) associados a técnicas de mapeamento digital são alternativas para obter informações a menores custos, entretanto, alguns testes são necessários para se fazer isso de forma mais eficiente. Este estudo objetivou comparar diferentes métodos para extrair informações de mapas de solos em escala detalhada usando árvores de decisão para mapear classes de solos em duas sub-bacias hidrográficas de Minas Gerais, validar os mapas em campo e usar o melhor método para extrapolar informações para áreas maiores, também validando estes mapas. Mapas de solos detalhados das sub-bacias do ribeirão Vista Bela (VBW) e ribeirão Marcela (MCW) foram utilizados como fonte de informação. Sete métodos para extrair informações de mapas foram comparados: polígono todo, eliminando 20 e 40 m das bordas dos polígonos, e com buffers em torno dos pontos amostrados com raios de 25 m, 50 m, 75 m e 100 m. O algoritmo Classification and Regression Trees (CART) foi utilizado para gerar árvores de decisão e permitir a criação de mapas de solos. A acurácia dos mapas foi avaliada através da acurácia global e índice Kappa. O melhor método foi utilizado para extrapolar informações para áreas maiores e esses mapas foram validados. Os melhores métodos para VCW e MCW foram, respectivamente, eliminando 20 m das bordas dos polígonos e buffer com raio de 25 m ao redor dos pontos. Mapas para áreas maiores foram obtidos usando esses métodos. Remoção de áreas de incerteza de mapas legados de solos contribuem para uma melhor modelagem e predição de classes de solos. A utilização de informações geradas neste trabalho permitiu a extrapolação validada de mapas de solos para regiões do entorno das sub-bacias hidrográficas.

ABSTRACT Existing soil maps (legacy data) associated with digital mapping techniques are alternatives to obtain information at lower costs, however, tests are required to do it more efficiently. This study had as objectives to compare different methods to extract information from detailed scale soil maps using decision trees for mapping soil classes at two watersheds in Minas Gerais, validate these maps in the field and use the best method to extrapolate information to larger areas, also validating these maps of larger areas. Detailed soil maps of Vista Bela creek (VBW) and Marcela creek (MCW) watersheds were used as source of information. Seven methods to extract information from maps were compared: the whole polygon, eliminating 20 and 40 m from the polygon boundaries, and with buffers around the sampled points with radii of 25 m, 50 m, 75 m, and 100 m. The Classification and Regression Trees (CART) algorithm was employed to create decision trees and enable creation of soil maps. Accuracy was assessed through overall accuracy and kappa index. The best method was used to extrapolate information to larger areas and maps were validated. The best methods for VCW and MCW were, respectively, eliminating 20 m from polygon edges and buffer of 25 m of radii from points. Maps for larger areas were obtained using these methods. Removing uncertainty areas from legacy soil maps contribute to better modeling and prediction of soil classes. Information generated in this work allowed for validated extrapolation of soil maps for regions surrounding the watersheds.

ABSTRACT The application of quantitative methods to digital soil and geomorphological mapping is becoming an increasing trend. One of these methods, Geomorphons, was developed to identify the ten most common landforms based on digital elevation models. This study aimed to make a quantitative assessment of the relationships between Geomorphons units, determined at three spatial resolutions and nine radii, and soil types and properties of two watersheds with different soil-landscape relationships in Brazil to help soil surveying and mapping under tropical conditions. The study was conducted at Lavrinha Creek (LCW) and Marcela Creek (MCW) watersheds, located in the state of Minas Gerais, Brazil. Spatial resolutions of 10, 20 and 30 m were the basis for generating Geomorphons at 9 radii of calculation for the watersheds. They were overlapped to detailed soil maps of the watersheds and a chi-square test was carried out to assess their relationship with soil types. Observation points were compared with the most highly correlated Geomorphons to also assess relationships with soil properties. Geomorphons with resolution of 30-m and radii of 20 and 50 cells, respectively for LCW and MCW, were more highly correlated with the variability of soil types, in accordance with the terrain features of these watersheds. The majority of observation points for each soil type was located in the same Geomorphon unit that was dominant when analyzing soil maps. There was less variability in soil properties between Geomorphon units, which was probably due to the highly weathered-leached stage of soils. Geomorphons can help to improve soil maps in tropical conditions when assessing soil variability due to its high correlation with tropical soil types variability.

RESUMO Os modelos hidrológicos são ferramentas que descrevem processos e permitem antecipar as ocorrências e tomar decisões, dessa forma, apresentam-se como uma importante ferramenta de gestão dos recursos hídricos, principalmente no contexto de bacias hidrográficas de pequeno porte. Assim, objetivou-se com este trabalho abordar diferentes estratégias de calibração e teste do modelo SWAT na simulação hidrológica da bacia hidrográfica do Rio das Mortes, MG. Foram avaliados 6 postos fluviométricos, com área de drenagem entre 272 e 6.070 km², para fins de gestão dos recursos hídricos. A avaliação do desempenho do modelo foi realizada usando os coeficientes de Nash-Sutcliffe (CNS) e percentual de viés (PBIAS). Os resultados obtidos dos índices estatísticos aplicados na análise de desempenho do modelo, qualificam o modelo hidrológico SWAT como adequado na simulação da vazão para a bacia hidrográfica do Rio das Mortes. As estratégias adotadas atestaram a aplicabilidade do modelo como ferramenta de gestão e planejamento dos recursos hídricos para bacias menores e sem dados, possibilitando o uso racional da água na bacia.

ABSTRACT Hydrological models are tools which describe processes and allow predicting the results of making management decisions, and are an important water resource management tool, especially for small-sized watersheds. This work had as an objective to test different calibration strategies and apply the SWAT model for hydrological simulation of the Mortes River Basin, MG. We evaluated 6 fluviometric stations, with drainage area between 272 and 6070 km² for purposes of water resource management. The evaluation of the model performance was conducted by using Nash-Sutcliffe coefficients (CNS) and percent bias (PBIAS). The results obtained from the statistic indices applied in the analysis of the model performance qualified the SWAT hydrological model as adequate for streamflow simulation in the Mortes River Basin. The adopted strategies attest to the applicability of the model as a management tool for water resources planning for other small-sized watersheds without data, in order to plan for rational water use.

ABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape.

ABSTRACT Digital soil mapping (DSM) permits continuous mapping soil types and properties through raster formats considering variation within soil class, in contrast to the traditional mapping that only considers spatial variation of soils at the boundaries of delineated polygons. The objective of this study was to compare the performance of SoLIM (Soil Land Inference Model) for two sets of environmental variables on digital mapping of saturated hydraulic conductivity and solum depth (A + B horizons) and to apply the best model on runoff risk evaluation. The study was done in the Posses watershed, MG, Brazil, and SoLIM was applied for the following sets of co-variables: 1) terrain attributes (AT): slope, plan curvature, elevation and topographic wetness index. 2) Geomorphons and terrain attributes (GEOM): slope, plan curvature, elevation and topographic wetness index combined with geomorphons. The most precise methodology was applied to predict runoff areas risk through the Wetness Index based on contribution area, solum depth, and saturated hydraulic conductivity. GEOM was the best set of co-variables for both properties, so this was the DSM model used to predict the runoff risk. The runoff risk showed that the critical months are from November to March. The new way to classify the landscape to use on DSM was demonstrated to be an efficient tool with which to model process that occurs on watersheds and can be used to forecast the runoff risk.

RESUMOHá a necessidade de se compreender os processos de formação do solo em diferentes paisagens para prever como as propriedades do solo irão responder às mudanças no uso do solo. Neste estudo, objetivou-se caracterizar Cambissolos em uma região montanhosa sob clima subtropical de altitude no Sudeste do Brasil, por meio de análises físicas, químicas e micromorfológicas do solo, sob mata nativa e pastagem. O solo sob pastagem apresentou maior densidade do solo do que sob mata, expressa pela intensa redução de macroporosidade na primeira condição. Em ambas as profundidades, os grãos de quartzo grosseiros são angulares, sugerindo ausência de quaisquer processos de transporte, confirmando, assim, uma pedogênese autóctone, a partir da rocha gnáissica subjacente. A maioria dos feldspatos se encontra intemperizada e não mais identificável, mas a alteração mineral é comumente vista ao longo de planos de clivagem e bordas de micas. Os resultados micromorfólogicos sugerem um estágio intermediário de intemperismo mineral e desenvolvimento do solo, o que está de acordo com as propriedades esperadas em Cambissolos.

ABSTRACTUnderstanding soil formation processes across different landscapes is needed to predict how soil properties will respond to land use change. This study aimed to characterize mountainous Inceptisols (Cambisols) under high altitude subtropical climate in southeastern Brazil, by soil physical, chemical and micromorphological analyses, under native forest and pasture. The soil under pasture had a greater bulk density than under forest, resulting in a severe reduction of macroporosity. At two depths, coarse quartz grains are angular, suggesting absence of transportational processes, thus confirming an autochthonous pedogenesis from the underlying gneissic rock. Most feldspars were weathered beyond recognition, but mineral alteration was commonly seen across cleavage plans and edges of micas. The micromorphological results suggest an intermediate stage of mineral weathering and soil development, which is in accordance with properties expected to be found in Inceptisols.

In soil surveys, it is usual to find profiles with an uncommon disposition of horizons. Dark horizons in depth might be either the consequence of erosion and redeposition of soil materials from upslope or an indication of the podzolization process, which forms a spodic horizon. Few laboratory analyses are known to characterize dark subsurface horizons which could allow for the differentiation of spodic from buried A horizons. Some researchers propose C-humic and C-fulvic acid fraction ratios and forms of carbon to analyze characteristics of these horizons. Therefore, this research aimed to characterize dark subsurface horizons found in soils under a Eucalyptus minimum tillage system in the state of Rio Grande do Sul, Brazil, and to relate soil organic carbon to landscape features in toposequences. The characterization was performed by using the following ratios: humic acid and fulvic acid fractions (Cha/Cfa); pyrophosphate extractable-C and organic carbon (Cp/OC); fulvic acid fraction and pyrophosphate extractable-C (Cfa/Cp), and fulvic acid fraction and organic carbon (Cfa/OC). Soil organic carbon was related to slope gradient and Geomorphons in a Geographic Information System (GIS). None of the horizons analyzed met the criteria required for spodic horizon classification, where Cha/Cfa < 0.50, Cfa/OC < 0.30, and the ratio Cp/OC ≥ 0.50 simultaneously with Cfa/Cp ≥ 0.50. A relationship was found between landscape features and soil organic carbon content. The methodology proved to be satisfactory for providing scientific support to field morphology classification of dark subsurface horizons, specifically in the case where they could be misinterpreted as spodic horizons.

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit.

No Brasil, levantamentos de solos em escalas maiores ainda são escassos e necessários para dar apoio mais adequado ao planejamento de atividades relacionadas a solos e ambientes em áreas menores. Em consequência, este trabalho apresenta algumas técnicas de mapeamento digital de solos que permitem a produção mais rápida de levantamentos de solos, além de ajustar a distribuição espacial contínua das propriedades do solo em categorias discretas, de acordo com a complexidade inerente da variabilidade dos mesmos, aumentando a acurácia de informações espaciais. A técnica aqui enfatizada é baseada em sistemas que empregam o conhecimento de um especialista, sob uso de lógica fuzzy e similaridade de vetores. Para isso, é proporcionada a contextualização de cada ferramenta para a predição de classes de solos e suas propriedades, assim como algumas opções de técnicas para a aquisição de conhecimentos. Tais ferramentas têm reduzido a inconsistência e custos associados aos tradicionais procedimentos manuais, utilizando uma relativamente baixa densidade de amostragem. Por outro lado, a técnica baseada no conhecimento de especialistas não é automatizada, e, assim como no método tradicional de levantamentos de solos, o conhecimento das relações solo-paisagem é insubstituível.

In Brazil, soil surveys in more detailed scale are still scarce and necessary to more adequately support the decision makers for planning soil and environment activities in small areas. Hence, this review addresses some digital soil mapping techniques that enable faster production of soil surveys, beyond fitting continuous spatial distribution of soil properties into discrete soil categories, in accordance with the inherent complexity of soil variation, increasing the accuracy of spatial information. The technique focused here is knowledge-based in expert systems, under fuzzy logic and vector of similarity. For that, a contextualization of each tool in the soil types and properties prediction is provided, as well as some options of knowledge extraction techniques. Such tools have reduced the inconsistency and costs associated with the traditional manual processes, relying on a relatively low density of soil samples. On the other hand, knowledge-based technique is not automatic, and just as the traditional soil survey, the knowledge of soil-landscape relationships is irreplaceable.