The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest ...
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The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest area in the semi - arid region of Black Sea backward region of Turkey. Some of the soil properties, including total nitrogen, SOC, soil organic matter, and bulk density were measured on a grid with a 50 m sampling distance on the top soil (0-15 cm depth). Accordingly, a total of 120 samples were taken from the four adjacent land uses. Data was analyzed using geostatistical methods. The methods used were: Block kriging (BK), co - kriging (CK) with organic matter, total nitrogen and bulk density as auxiliary variables and inverse distance weighting (IDW) methods with the power of 1, 2 and 4. The methods were compared using a performance criteria that included root mean square error (RMSE), mean absolute error (MAE) and the coefficient of correlation (r). The one - way ANOVA test showed that differences between the natural (0.6653 +/- 0.2901) - plantation forest (0.7109 +/- 0.2729) areas and the grassland (1.3964 +/- 0.6828) - cultivated areas (1.5851 +/- 0.5541) were statistically significant at 0.05 level (F = 28.462). The best model for describing spatially variation of SOC was CK with the lowest error criteria (RMSE = 0.3342, MAE = 0.2292) and the highest coefficient of correlation (r = 0.84). The spatial structure of SOC could be well described by the spherical model. The nugget effect indicated that SOC was moderately dependent on the study area. The error distributions of the model showed that the improved model was unbiased in predicting the spatial distribution of SOC. This study's results revealed that an explanatory variable linked SOC increased success of spatial interpolation methods. In subsequent studies, this case should be taken into account for reaching more accurate outputs. (C) 2017 Elsevier Ltd. All rights reserved.
Detailed maps of regional spatial distribution of soil organic carbon (SOC) are needed to guide sustainable soil uses and management decisions. interpolationmethods based on spatial auto-correlations, environmental c...
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Detailed maps of regional spatial distribution of soil organic carbon (SOC) are needed to guide sustainable soil uses and management decisions. interpolationmethods based on spatial auto-correlations, environmental covariates, or hybrid methods are commonly used to predict SOC maps. Many of these methods perform well for gentle terrains. However, it is unknown how these methods perform to capture SOC variations in complex terrains, especially areas of which land uses are interrupted by human activities, such as the Loess Plateau of China. This study compared four interpolations or predictive methods including ordinary kriging (OK), regression kriging, ordinary kriging integrated with land-use type (OK_LU) and a soil land inference model (SoLIM). The purpose of this study is to find appropriate methods, which are suitable to the complex terrain in Loess Plateau region of China. The study area was a typical watershed in Loess Plateau with complex hilly-gully terrain and various land-use types. A field sampling dataset of 200 points was partitioned into 1/2 for model building and 1/2 for accuracy validation in a random way. Nine environmental covariates were selected: land-use types, digital elevation model, solar radiation, slope degree, slope aspect, plan curvature, profile curvature, surface area ratio, and topographic wetness index. The mean absolute percentage error, root mean square error, and goodness-of-prediction statistic value were selected to evaluate mapping results. The results showed that the use of easily obtained environmental covariates, land-use types and terrain variables improved accuracies of SOC interpolation, which will be of interests for related research of similar environments in the Loess Plateau. SoLIM and OK_LU can be two suitable and efficient methods, which produced detailed, reasonable maps with higher accuracy and prediction effectiveness, for the study area and similar areas in the Loess Plateau.
Rainfall is regarded as the most important input for the hydrology and nonpoint source (H/NPS) models and uncertainty related to rainfall is generally recognized as a major challenge in watershed modeling. In this pap...
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Rainfall is regarded as the most important input for the hydrology and nonpoint source (H/NPS) models and uncertainty related to rainfall is generally recognized as a major challenge in watershed modeling. In this paper, we focus on the impact of spatial rainfall variability on H/NPS modeling of a large watershed. The uncertainty introduced by spatial rainfall variability was determined using a number of commonly-used interpolationmethods: (1) the Centroid method;(2) the Thiessen Polygon method;(3) the Inverse Distance Weighted (IDW) method;(4) the Dis-Kriging method;and (5) the Co-Kriging method. The Soil and Water Assessment tool (SWAT) was used to quantify the effect of rainfall spatial variability on watershed H/NPS modeling of the Daning watershed in China. Results indicated that these interpolationmethods could contribute significant uncertainty in spatial rainfall variability and the carry-magnify effect caused even larger uncertainty in the H/NPS modeling. This uncertainty was magnified from hydrology modeling (stream flow) into NPS modeling (sediment, TP, organic nitrogen (N) and dissolved N). This study further suggested that H/NPS prediction uncertainty relating to spatial rainfall variability was scale-dependent due to the averaging effect of spatial heterogeneity. From a practical point of view, a global interpolationmethod, such as IDW and Kriging, as well as elevation data derived from a digital elevation model (DEM), should be included into the H/NPS models for reliable predictions in larger watersheds. (C) 2012 Elsevier B.V. All rights reserved.
On cherche souvent à établir la distribution spatiale des précipitations sur une grille régulière. Les méthodes d’interpolationspatiale sont couramment utilisées pour estimer de te...
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On cherche souvent à établir la distribution spatiale des précipitations sur une grille régulière. Les méthodes d’interpolationspatiale sont couramment utilisées pour estimer de telle distribution à partir des observations au sol d’un réseau de mesures. Dans les régions montagneuses, l’estimation des précipitations en altitude reste un défi et les résultats des interpolations spatiales doivent être contrôlés avec le maximum d’attention. Nous proposons dans cet article une méthodologie de validation d’un champ de précipitation à trois niveaux successifs. Elle est appliquée dans le contexte d’une région semi-aride montagneuse, le Norte Chico au Chili (26°S-32°S). L’application de cette méthodologie a permis de mettre en évidence les bénéfices apportés par une méthode d’interpolation développée par Valéry [1] pour les zones de montagne. En particulier, le bilan hydrologique des bassins versants les plus en altitude est rendu plus *** precipitations on a regular grid is often required for hydrological and ecological modelling. The spatial interpolation methods are generally used to estimate such a distribution from ground-based measurements. In the case of mountainous areas, the estimation of precipitation amounts is still a challenging task and the results of spatialinterpolation should be verified as much as possible. Here we describe a three-steps method for the validation of a precipitation map. This is used in the context of a mountainous semi-arid region, the Norte Chico in Chile (26°S-32°S). The implementation of this validation method showed the benefits of an interpolationmethod developed by Valéry [2010] for mountainous areas. The hydrological balance of the high-altitude watersheds is now more realistic.
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