关键词：
land use change;runoff;climate change;SWAT model
摘要：
<jats:p>Quantitative assessment of the impact of land use and climate change on hydrological processes is of great importance to water resources planning and management. The main objective of this study was to quantitatively assess the response of runoff to land use and climate change in the Zhengshui River Basin of Southern China, a heavily used agricultural basin. The Soil and Water Assessment Tool (SWAT) was used to simulate the river runoff for the Zhengshui River Basin. Specifically, a soil database was constructed based on field work and laboratory experiments as input data for the SWAT model. Following SWAT calibration, simulated results were compared with observed runoff data for the period 2006 to 2013. The Nash-Sutcliffe Efficiency Coefficient (NSE) and the correlation coefficient (R2) for the comparisons were greater than 0.80, indicating close agreement. The calibrated models were applied to simulate monthly runoff in 1990 and 2010 for four scenarios with different land use and climate conditions. Climate change played a dominant role affecting runoff of this basin, with climate change decreasing simulated runoff by −100.22% in 2010 compared to that of 1990, land use change increasing runoff in this basin by 0.20% and the combination of climate change and land use change decreasing runoff by 60.8m3/s. The decrease of forestland area and the corresponding increase of developed land and cultivated land area led to the small increase in runoff associated with land use change. The influence of precipitation on runoff was greater than temperature. The soil database used to model runoff with the SWAT model for the basin was constructed using a combination of field investigation and laboratory experiments, and simulations of runoff based on that new soil database more closely matched observations of runoff than simulations based on the generic Harmonized World Soil Database (HWSD). This study may provide an important reference to guide management decisions for this and similar watersheds.</jats:p>
作者机构：
[薛毅; 尹泽润; 盛浩; 马颢榴; 周清; 张杨珠] College of Resources and Environment, Hunan Agricultural University, Changsha;410128, China;[宋达清] Soil and Fertilizer Station of Zhuzhou County, Lukou;412000, China;[薛毅; 尹泽润; 盛浩; 马颢榴; 周清; 张杨珠] 410128, China
通讯机构：
College of Resources and Environment, Hunan Agricultural University, Changsha, China
摘要：
<jats:title>Abstract</jats:title><jats:p>Identification of the dominant factors controlling rice (<jats:italic>Oryza sativa</jats:italic> L.) yield is of paramount importance to improve fertilizer use efficiency, effectively promote rice productivity, and help ensure food security. The objective of this study was to quantify the influence of geographic attributes, soil properties, climatic, and fertilizer types on annual rice yield and to identify the dominant control factors in southern China. In total, 2010 soil samples were collected in the selected areas and were analyzed for 34 factors that potentially influenced rice yield. Because these factors exhibit multicollinearity, partial least squares regression (PLSR) was used to elucidate the linkages between rice yield and the 34 measured variables. The first‐order factors were identified by calculating the variable importance for the projection (VIP). The variables with high VIP values are the most relevant for explaining the dependent variables. Results indicated that the geographic attributes, climatic and fertilizer types exerted substantial influence on rice yield and explained 57 to 85% of the variation in rice yield. According to the VIP values, the following are the dominant first‐order factors controlling rice yield: longitude, latitude, organic potassium fertilizer, accumulated temperature≥10°C, chemical potassium fertilizer, organic nitrogen fertilizer, straw incorporation, chemical nitrogen fertilizer, accumulated temperature≥0°C, and organic phosphate fertilizer. These results indicate that the PLSR approach is beneficial as it partially eliminates the correlation of the variables and reduces bias regarding the contribution of the factors to rice yield. This approach could be applied to other climatic zones or cropping systems.</jats:p>
作者机构：
[宋佳龄; 段良霞; 周清] College of Resources & Environment, Hunan Agricultural University, Changsha;410128, China;[盛浩; 张杨珠] Institute of Soil Science, Hunan Agricultural University, Changsha;[周萍] Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha;410125, China
摘要：
Soil organic matter (SOM) is an important index to evaluate soil fertility. Knowing the spatial distribution of SOM and its controlling factors at different scales is basic to sustainable farmland management. The variability was explored mostly in plain farmlands or at small scales in previous studies. In the present study, combined with anisotropy analysis (AA) and discrete wavelet transform (DWT), we examined the spatial variability of SOM and its controlling factors at various scales in a mountainous area. Transect with dominant directions (major axis and minor axis) of SOM variability was extracted using AA and then the scale-specific variability was examined using DWT. Dominant factors of SOM variability at different scales were identified using correlation coefficients between SOM at different scales and various soil environmental factors. The results showed that the major axis along which SOM varied the most was 24° south by west, consistent with the strike of Wuling Mountains. The minor axis was perpendicular to the major axis direction. DWT separated the SOM variations into nine scale components (eight details, D1 through D8, and one approximation, A8) along the major axis and into eight scale components (seven details, D1 through D7, and one approximation, A7) along minor axis. The largest-scale component (A8 in major axis and A7 in minor axis) explained the most variance of SOM along both axes, accounting for half of the total variance. Compared with the original SOM before separation of scale components (undecomposed SOM), the scale components showed significant correlation with environmental factors. Both elevation and mean annual precipitation had positive correlation with SOM at large scales. However, there was a negative correlation between SOM and mean annual temperature. This indicates that the topography and local climate may have a stronger influence in controlling SOM spatial distribution in mountain regions. The relationship provides important information on environmental covariate selection in mapping soil resource. The combination of AA and DWT shows promise quantifying SOM spatial distribution and its control factors at different scales in mountainous areas.
会议名称：
2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)
会议时间：
August 2018
会议地点：
Hangzhou, China
会议论文集名称：
2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)
关键词：
cover and management factor;Spatio-temporal change;land use;vegetation coverage;Hunan Province
摘要：
Soil erosion is an important research issue because it can result in serious hazards, such as land quality degrading, flood frequency increasing and deposit in rivers and lakes. The factor of cover and management is a parameter of the soil erosion prediction model and it is the ratio of soil erosion of the present cover and management to continuous clear tillage. Good vegetation cover and the effective land management can control soil erosion to some extent. Quantitative assessment the factor of cover and management in a region scale can provide the cover and management data and some good suggestion for the land use and soil conservation planning. This study chooses Hunan Province in the central and southern China which has no relevant research till now. With the land use and vegetation coverage data of 30 meters resolution extracted from Landsat images in 2000, 2005, 2010 and 2015, the cover and management factor of this region is evaluated and the spatial-temporal characteristics of land use and the factor is analyzed by using the evaluation method, GIS spatial analysis and statistical analysis. The results are as follows. Because the main terrains are mountains and hills which account for 70 percent of the whole province area, the main land use types are forest land and grassland. The mean of cover and management factor is 0.06739, 0.06783, 0.06876 and 0.07421 in 2000, 2005, 2010 and 2015 respectively. The factor increased about 10 percent from 2000 to 2015. The change of cover and management factor may increase soil losses risk if the other factors affecting soil losses are not taken into account.
