Assessment of Quarterly, Semiannual and Annual Models to Forecast Monthly Rainfall Anomalies: The Case of a Tropical Andean Basin

Rainfall forecasting is essential to manage water resources and make timely decisions to mitigate adverse effects related to unexpected events. Considering that rainfall drivers can change throughout the year, one approach to implementing forecasting models is to generate a model for each period in which the mechanisms are nearly constant, e.g., each season. The chosen predictors can be more robust, and the resulting models perform better. However, it has not been assessed whether the approach mentioned above offers better performance in forecasting models from a practical perspective in the tropical Andean region. This study evaluated quarterly, semiannual and annual models for forecasting monthly rainfall anomalies in an Andean basin to show if models implemented for fewer months outperform accuracy; all the models forecast rainfall on a monthly scale. Lagged rainfall and climate indices were used as predictors. Support vector regression (SVR) was used to select the most relevant predictors and train the models. The results showed a better performance of the annual models mainly due to the greater amount of data that SVR can take advantage of in training. If the training of the annual models had less data, the quarterly models would be the best. In conclusion, the annual models show greater accuracy in the rainfall forecast.

Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

The Change in Environmental Variables Linked to Climate Change Has a Stronger Effect on Aboveground Net Primary Productivity Than Does Phenological Change in Alpine Grasslands

More and more studies have focused on responses of ecosystem carbon cycling to climate change and phenological change, and aboveground net primary productivity (ANPP) is a primary component of global carbon cycling. However, it remains unclear whether the climate change or the phenological change has stronger effects on ANPP. In this study, we compared the effects of phenological change and climate change on ANPP during 2000-2013 across 36 alpine grassland sites on the Tibetan Plateau. Our results indicated that ANPP showed a positive relationship with plant phenology such as prolonged length of growing season and advanced start of growing season, and environmental variables such as growing season precipitation (GSP), actual vapor pressure (E_a), relative humidity (RH), and the ratio of GSP to ≥5°C accumulated temperature (GSP/AccT), respectively. The linear change trend of ANPP increased with that of GSP, E_a, RH, and GSP/AccT rather than phenology variables. Interestingly, GSP had the closer correlation with ANPP and meanwhile the linear slope of GSP had the closer correlation with that of ANPP among all the concerned variables. Therefore, climate change, mainly attributed to precipitation change, had a stronger effect on ANPP than did phenological change in alpine grasslands on the Tibetan Plateau.

Indication of the Two Linear Correlation Methods Between Vegetation Index and Climatic Factors: An Example in the Three River-Headwater Region of China During 2000–2016

The within-growing-season correlations (WGSC) and the inter-growing-season correlations (IGSC) are widely used linear correlation analysis methods between vegetation index and climatic factors (such as temperature, precipitation, and so on). The WGSC method usually calculates the linear correlation coefficient between vegetation index and climatic factors of each month in all the growing seasons, for instance, whether vegetation index or temperature had data of 204 months (12 months × 17 years) during 2000–2016 to get the WGSC. The IGSC calculates the linear correlation coefficient between the vegetation index and climatic factors in the same month of each growing season among all the years, for example, only 17 couples’ data of vegetation index and temperature during 2000–2016 were used to get the linear correlation of IGSC. What is the difference between the results of the two methods and why do the results show that difference? Which is the more suitable method for the analysis of the relationship between the vegetation index and climatic conditions? To clarify the difference of the two methods and to explore more about the relationship between the vegetation index and climatic factors, we collected the data of 2000–2016 moderate resolution imaging spectroradiometer (MODIS) 13A1 normalized difference vegetation index (NDVI) and the meteorological data-temperature and precipitation, then calculated WGSC and IGSC between NDVI and the climatic factor in three river-headwater regions of China. The results showed that: (1) As for WGSC, the more of the years included, the higher the correlation coefficient between NDVI and the temperature/precipitation. The correlation coefficient of WGSC is dependent on how many years’ the data were included, and it was increased with the more year’s data included, while the correlation coefficients of IGSC are relatively independent on the amount of the data; (2) the WGSC showed a pseudo linear correlation between NDVI and climatic conditions caused by the accumulation of data amount, while the IGSC can more accurately indicate the impact of climatic factors on vegetation since it did not rely on the data amount.