Human activities can affect soil nutrients, thereby influencing river water quality. The spatial pattern of precipitation also impacts distributions of water quality. In this paper, we employed a method that combines point survey, soil, and water quality data to analyze the spatial relationships between precipitation, soil nutrient and water quality in the basin on the basis of field surveys and laboratory analysis. The ordinary kriging method was applied to interpolate the precipitation and soil data, and the spatial pattern was analyzed. The water samples on the main stream and soil samples in the field were collected during both the dry and rainy seasons to analyze the water quality and soil nutrients. The results indicate: (1) The water quality in the dry season is better than that in the rainy season, the water quality in the upper reaches is better than that in the lower reaches, and agricultural activity is the direct source of water pollution. (2) The precipitation in the rainy and dry seasons is differente and the dilution effect of precipitation on pollutant concentrations and transport of water flow affect the spatial distribution of water quality. (3) There is a significant difference in the spatial pattern of soil nutrients between the dry and rainy seasons, and the soil nutrient content and the surface runoff directly affect the water quality. Soil nutrients are affected by human activities, and they potentially act as nonpoint source (NPS) pollution in this river basin. To improve the water quality, suitable agriculture measures need to be implemented.

Based on soil sampling data from the dry season and the rainy season, the spatial heterogeneity and spatial pattern of soil nutrients in the Mun River Basin, Thailand, were studied and the seasonal variation in soil nutrients was analyzed using classical statistical methods and geostatistical methods. The soil nutrient content in the Mun Basin showed moderate and strong variations, and the descending order of soil variability was as follows: available phosphorous (AP) > electric conductivity (EC) > soil organic matter (SOM) > total nitrogen (TN) > pH value in the dry season, with AP showing strong variation, and EC > AP > SOM > TN > pH in the rainy season, with EC showing strong variation. Different soil nutrients and different soil properties had different spatial variation characteristics, and their corresponding best-fitting models were also different. Based on the nugget (C₀), sill (C₀ + C), and range (A), spatial analysis was performed for the soil nutrients, pH, and EC in the dry season and in the rainy season. Analysis based on kriging spatial interpolation data showed that pH, SOM, TN, and EC had convex or concave distributions, whereas AP had a patchy distribution. Terrain, vegetation, and human disturbance are the main factors that contribute to the differences in the soil nutrient pattern of the Mun River Basin.

Land use/cover change (LUCC) is one of the major factors influencing the storage of ecosystem carbon. The carbon storage in Qinghai-Tibet Plateau, the world’s highest plateau, is affected by a combination of many factors. Using MCD12Q1 land classification data, aboveground biomass, belowground biomass, soil carbon and humus carbon data, as well as field sampling data for parameters verification, we applied the InVEST model to simulate the ecosystem carbon storage and the impacts of driving factors. The field survey samples were used to test the regression accuracy, and the results confirmed that the model performance was reasonable and acceptable. The main conclusions of this study are as follows: From 2001 to 2010, carbon storage in the Qinghai-Tibet Plateau increased by 10.39 billion t when assuming that the carbon density in each land cover type was constant. Changes of the land cover types caused carbon storage to increase by 116 million t, which contributed 13.82% of the dynamic carbon storage. Consequently, changes in carbon density accounted for 86.18% of the carbon storage change. In addition, we investigated the soil organic matter and aboveground biomass characteristics between 2012 and 2014 and found that the influences of fencing and dung on carbon storage were positive.

The Chinese Loess Plateau is an ecologically fragile and sensitive area. The carbon storage dynamics in this region and the contributions from land use/land cover change (LUCC) and carbon density from 2000 to 2010 were analyzed in this paper. Normalized difference vegetation index (NDVI), biomass and soil carbon data in 2000 were used for regression analysis of biomass and soil carbon, and an inversion analysis was used to estimate biomass and soil carbon in 2005 and 2010. Quadrat data, including aboveground biomass and soil organic carbon, were used to calibrate the model output. Carbon storage and sequestration were calculated by the InVEST toolset with four carbon pools, including aboveground biomass, belowground biomass, dead wood and soil carbon. The results showed that carbon storage increased steadily from 2000 to 2010, increasing by 0.260 billion tons, and that woodland area increased and arable land decreased; the overall trend in land cover improved, but the improvement was not pronounced. Carbon storage in the Loess Plateau was correlated with geographical factors. We found that when assuming a constant carbon density, carbon storage decreased, accounting for −1% of the carbon storage dynamics. When assuming no land conversion, carbon storage increased, accounting for 101% of the carbon storage dynamics.

The Mun River Basin is one of Thailand’s major grain-producing areas, but the production is insufficient, and most of the cultivated lands are rain-fed and always unused in the dry season. All this makes it necessary to determine the status of soil nutrients and soil quality in the dry season to improve soil conditions, which will be useful for cultivation in the farming period. The aim of this study was to construct a soil-quality assessment based on soil samples, and in the process the minimum data set theory was introduced to screen the assessment indicators. The geographically weighted regression method was used to complete the spatial interpolation process of indicators, and the fuzzy logic model was constructed to evaluate the soil quality. The results showed that the spatial distributions of soil quality and indicators were similar. The soil quality was the best in the upstream while poor in the downstream, and the dry fields in the west and the forests in the east of the basin were better than other areas nearby. However; the soil qualities of paddy fields in the middle and east of the basin were poor due to the lack of soil nutrient supply when the fields were unused