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the multivariate linear regression analysis. 2.2.2. Multivariate linear regression analysis To examine the roles of SST and land surface (i.e., NDVI) variables in summer rainfall variability over the Sahel, we implement the multivariate linear regression analysis. We first apply the multivariate linear regression model with SST alone and then integrate the NDVI information. In the multivariate regression model, the area-averaged mean summer precipitation over the Sahel is used as the dependent
the multivariate linear regression analysis. 2.2.2. Multivariate linear regression analysis To examine the roles of SST and land surface (i.e., NDVI) variables in summer rainfall variability over the Sahel, we implement the multivariate linear regression analysis. We first apply the multivariate linear regression model with SST alone and then integrate the NDVI information. In the multivariate regression model, the area-averaged mean summer precipitation over the Sahel is used as the dependent
was analyzed. The trend in heavy rainfall frequency was determined using a least squares regression. Based on the model assumptions, a linear distribution regression was found to be acceptable. The regression line represents the long-term trend and determines whether the number of extreme events has or has not increased over the time period. 3. Results and analysis 3.1. PRISM rainfall data analysis Figure 3 shows the population densities for 1990 and 2010 and their difference. The high
was analyzed. The trend in heavy rainfall frequency was determined using a least squares regression. Based on the model assumptions, a linear distribution regression was found to be acceptable. The regression line represents the long-term trend and determines whether the number of extreme events has or has not increased over the time period. 3. Results and analysis 3.1. PRISM rainfall data analysis Figure 3 shows the population densities for 1990 and 2010 and their difference. The high
), irrigation (200 mm, with 100 mm applied at both jointing and flowering stage), and sowing density (70 000 plants per hectare). These conditions serve as the “best currently available management practices” during APSIM simulations in our study. Table 2. Soil bulk density of different layers of soil for each CZ and optimal level in northeast China (g cm −3 ). 2.4. Data analyses In our analysis, we define potential farmers’ yield Y pf , potential farmers’ yield with optimal soil Y pf+OS , high
), irrigation (200 mm, with 100 mm applied at both jointing and flowering stage), and sowing density (70 000 plants per hectare). These conditions serve as the “best currently available management practices” during APSIM simulations in our study. Table 2. Soil bulk density of different layers of soil for each CZ and optimal level in northeast China (g cm −3 ). 2.4. Data analyses In our analysis, we define potential farmers’ yield Y pf , potential farmers’ yield with optimal soil Y pf+OS , high
the exception of wintertime minimum temperatures. Figure 7. Southeast U.S. seasonal and annual temperature trends (1920–92). Table 3. Regression statistics for temperature trend analysis 1920–92. Bold values indicate trend is significantly nonzero at p value = 0.05. 4.4. Satellite skin temperature Next, the difference in temperatures between forested and cropland areas are demonstrated. A look at the observed MODIS skin temperature data shows that forested areas are indeed cooler. Figure 8
the exception of wintertime minimum temperatures. Figure 7. Southeast U.S. seasonal and annual temperature trends (1920–92). Table 3. Regression statistics for temperature trend analysis 1920–92. Bold values indicate trend is significantly nonzero at p value = 0.05. 4.4. Satellite skin temperature Next, the difference in temperatures between forested and cropland areas are demonstrated. A look at the observed MODIS skin temperature data shows that forested areas are indeed cooler. Figure 8
and analysis of the regional urban extension. Figure 4. MODIS IGBP land-cover classification. Urban classes are shown in red and nonurban classes are shown in green. New urban subclasses result from this process by use of the Clustering for Large Applications (CLARA) algorithm on the broadband albedos. The CLARA algorithm extends the k -medoids approach for a large number of objects by clustering a sample from the dataset and then assigning all objects in the dataset to these clusters. The k
and analysis of the regional urban extension. Figure 4. MODIS IGBP land-cover classification. Urban classes are shown in red and nonurban classes are shown in green. New urban subclasses result from this process by use of the Clustering for Large Applications (CLARA) algorithm on the broadband albedos. The CLARA algorithm extends the k -medoids approach for a large number of objects by clustering a sample from the dataset and then assigning all objects in the dataset to these clusters. The k
inversion framework and how they are connected. The data and analysis steps are presented in detail in section 2 . Figure 1. Flow diagram of the framework applied in our study with the consecutive steps needed to provide the input for the Bayesian inversion. Please see text for details. 2. Data and methods 2.1. Tower network and atmospheric observation data Oregon is characterized by significant micro- to mesoscale variability in climate and vegetation characteristics. The crest of the Cascade Mountain
inversion framework and how they are connected. The data and analysis steps are presented in detail in section 2 . Figure 1. Flow diagram of the framework applied in our study with the consecutive steps needed to provide the input for the Bayesian inversion. Please see text for details. 2. Data and methods 2.1. Tower network and atmospheric observation data Oregon is characterized by significant micro- to mesoscale variability in climate and vegetation characteristics. The crest of the Cascade Mountain
requires approximately 30 years to be in a reasonable state of equilibrium. Therefore, in order to exclude spinup effects, the first 40 years of the simulations were discarded from the subsequent analysis. The climatological-mean difference between the CurVeg and PotVeg experiments (i.e., CurVeg minus PotVeg) describes the effects of LULCC. LULCC led to significant changes in tree, crop, shrub, and grass PFTs between the current and potential vegetation covers considered ( Figure 1 ). Tree PFTs show
requires approximately 30 years to be in a reasonable state of equilibrium. Therefore, in order to exclude spinup effects, the first 40 years of the simulations were discarded from the subsequent analysis. The climatological-mean difference between the CurVeg and PotVeg experiments (i.e., CurVeg minus PotVeg) describes the effects of LULCC. LULCC led to significant changes in tree, crop, shrub, and grass PFTs between the current and potential vegetation covers considered ( Figure 1 ). Tree PFTs show
