An objective analogue prediction model for tropical cyclone (TC) track is put forward that comprehensively assesses the environmental field. With the parameters of the tropical cyclone and environmental field at initi...An objective analogue prediction model for tropical cyclone (TC) track is put forward that comprehensively assesses the environmental field. With the parameters of the tropical cyclone and environmental field at initial and future time, objective analogue criteria are set up in the model. Analogous samples are recognized by comprehensive assessment to historical TC cases for similarity with multivariate criteria,using non-linear analogue indexes especially defined for the purpose. When the historical tracks are coordinateconverted and weighted with reference to analogue indexes,forecast tracks are determined. As shown in model verification and forecast experiments, the model has forecasting skill.展开更多
Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil...Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.展开更多
文摘An objective analogue prediction model for tropical cyclone (TC) track is put forward that comprehensively assesses the environmental field. With the parameters of the tropical cyclone and environmental field at initial and future time, objective analogue criteria are set up in the model. Analogous samples are recognized by comprehensive assessment to historical TC cases for similarity with multivariate criteria,using non-linear analogue indexes especially defined for the purpose. When the historical tracks are coordinateconverted and weighted with reference to analogue indexes,forecast tracks are determined. As shown in model verification and forecast experiments, the model has forecasting skill.
基金Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research(No.201105020)National Natural Science Foundation of China(No.41471178,41023010,41431177)National Key Technology Innovation Project for Water Pollution Control and Remediation(No.2013ZX07103006)
文摘Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
