Susceptibility to erosion risks in soils dedicated to the pineapple cultivation in the state of Valle del Cauca, Colombia

In Colombia almost half of the soils are prone to erosion, where Valle del Cauca is one of the most affected departments with respect to its severity. In order to identify the susceptibility to erosion risks in terms of the rainfall erosivity and the incidence of the topographic factor in the main pineapple producing municipalities of the department, a study was carried out that contemplated the historical climatological information of more than 50 years, for which all available weather stations in the area were selected. The erosivity index (R–EI30), the modified Fournier index (MFI), and the topographic factor (LS) were estimated. The results indicate that the average MFI values ranged between (91.31 and 582.79) which correspond to the moderate, high and very high categories, the results of R-EI30 presented moderate, high, very high and extremely severe values (5076.91 MJ.mm.ha-1 - 22718.83 MJ.mm.ha-1), while the highest values of the topographic factor (with slopes up to 81°) coincide with the upper part of the river basin Dagua, predominantly in the municipality of Dagua. These values indicate that the soils in the area are susceptible to erosion risks depending on the rainfall erosivity and the topographic factor at a moderate, high and very high level, which can generate significant soil losses, and therefore they should be considered when establishing a pineapple crop.

Global component analysis of errors in five satellite-only global precipitation estimates

Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.

Matching area selection of an underwater terrain navigation database with fuzzy multi-attribute decision making method

Selecting suitable underwater terrain navigation matching areas is a prerequisite for building an underwater terrain navigation database, which is important for vehicles operating underwater. By using information features to evaluate underwater terrain matching areas, vague sets are proposed to evaluate matching performance. Mathematical models of matching area features are built and topographic factor eigenvalues are obtained. With the topographic factor eigenvalues, fuzzy relationships between factor sets and judge sets are calculated. Vague set uses membership functions and non-membership functions to define the influence of topographic factor eigenvalues on matching suitability. Simulation results demonstrate that vague set theory can overcome the deficiency of single value in fuzzy sets and define the effect of geographic characteristics for matching performance. Based on vague set method, selection rules for terrain navigation matching areas in underwater terrain database are put forward.

ALTERNATIVA PARA O CÁLCULO AUTOMÁTICO E ESPACIALIZADO DO FATOR TOPOGRÁFICO DA USLE EM BACIAS HIDROGRÁFICAS

ALTERNATIVA PARA O CÁLCULO AUTOMÁTICO E ESPACIALIZADO DO FATOR TOPOGRÁFICO DA USLE EM BACIAS HIDROGRÁFICAS FRANCISCO EMANOEL FIRMINO GOMES1; GEORGE LEITE MAMEDE2 E FERNANDO BEZERRA LOPES3 1Departamento de engenharia agrícola/UFC, Doutorando em engenharia agrícola, Fortaleza, CE, Fone:(85)99238-2819, CEP:60440-900, e-mail: [email protected]. 2Instituto de Engenharias e Desenvolvimento Sustentável/ UNILAB, Professor Doutor, Redenção, CE, CEP: 62790-000, e-mail: [email protected] 3Departamento de Engenharia Agrícola, UFC, Professor Doutor, Fortaleza, CE, CEP:60440-900, e-mail: [email protected] 1 RESUMO Dentre os fatores da Universal Soil Loss Equation (USLE), o fator topográfico é que menos se aproxima da realidade e, em geral, os modelos apresentam elevada complexidade para sua determinação. Neste estudo, portanto objetivou-se calcular o fator topográfico da USLE de maneira simplificada usando técnicas de Sistema de Informações Geográficas (SIG). Para tanto, foi utilizado dados do Modelo Digital de Elevação - MDE obtido a partir do (SRTM -Shuttle Radar Topography Mission), assim foram calculadas as declividades e os comprimentos de rampas usando processamento dos dados matriciais do MDE, para então estimar o fator topográfico. Os valores de fator topográfico variaram de 0,21 a 9,88 com média de 1,97. As técnicas de sistema de informação geográficas mostraram-se eficientes para o cálculo do fator topográfico a partir do MDE. Palavras-chave: erosão, topografia de encosta, geoprocessamento. GOMES, F. E. F.; MAMEDE, G. L.; LOPES, F. B. ALTERNATIVE FOR THE AUTOMATIC AND SPACIALIZATION OF USLE TOPOGRAPHIC FACTOR IN WATERSHEDS 2 ABSTRACT Among the factors of the Universal Soil Loss Equation (USLE), the topographic factor is that it is less close to reality and, usually, the models used for its determination presents high complexity. In this study, therefore, the main objective was to calculate the topographic factor of the USLE in a simplified way using techniques geographic information system (sig). For that, data from the Digital Elevation Model – DEM was used, derived from the SRTM (Shuttle Radar Topography Mission), so slopes and slope length were calculated by processing of the DEM matrix data, so the topographic factor was estimated. The topographic factor values varying from 0.21 to 9.88 with an average of 1.97. The GIS techniques showed efficient for estimating the topographic factor derived from DEM data base. Keywords: erosion, topography of hillside, geoprocessing.