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Toponyms serve as symbols of regional culture and thus reflect the history, habitat and environment of a place. This study aims to (1) visualize the distributions of the four ethnic groups and landscape features in Guangdong of China using a toponym mapping method, of which results were presented at 1:6,400,000 scale and (2) to explore the changes by comparing contemporary data, of which results were presented at 1:3,200,000 scale. These maps provide a toponymic aspect to explore the historical evolution of ethnic groups and landscape features. ARTICLE HISTORY

Summer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982-2010 are used to make the forecast. The results show that the SST-based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations.

Autumn vegetation phenology plays a critical role in identifying the end of the growing season and its response to climate change. Using the six vegetation indices retrieved from moderate resolution imaging spectroradiometer data, we extracted an end date of the growing season (EOS) in the temperate deciduous broadleaf forest (TDBF) area of China. Then, we validated EOS with the ground-observed leaf fall date (LF) of dominant tree species at 27 sites and selected the best vegetation index. Moreover, we analyzed the spatial pattern of EOS based on the best vegetation index and its dependency on geo-location indicators and seasonal temperature/precipitation. Results show that the plant senescence reflectance index-based EOS agrees most closely with LF. Multi-year averaged EOS display latitudinal, longitudinal and altitudinal gradients. The altitudinal sensitivity of EOS became weaker from 2000 to 2012. Temperature-based spatial phenology modeling indicated that a 1 K spatial shift in seasonal mean temperature can cause a spatial shift of 2.4–3.6 days in EOS. The models explain between 54% and 73% of the variance in the EOS timing. However, the influence of seasonal precipitation on spatial variations of EOS was much weaker. Thus, spatial temperature variation controls the spatial patterns of EOS in TDBF of China, and future temperature increase might lead to more uniform autumn phenology across elevations.

Long-range precipitation forecasting is crucial for flooding control and water resources management. However, making precise forecasting is rather difficult due to the complex climatic factors and large uncertainties arising from long lead times. Sea surface temperature anomaly (SSTA) is one of the strongest signals that influence regional precipitation, often used for the development of precipitation forecasts. Traditional models using SSTA for precipitation forecasting usually screen SSTA over fixed oceanic zones and neglect its preceding temporal fluctuation information. In this study, we introduce a multipole SSTA index and the preceding fluctuation modes of SSTA to develop a monthly precipitation forecasting model, which is applied to the upper Yangtze River basin in China where monthly precipitation during May-October for the period of 1961-2020 are forecasted. Results show that more significant SSTA poles correlated with precipitation are found for September than for the other months. The new approach is able to forecast monthly precipitation for May-October in the basin, particularly for September. It outperforms traditional statistical and dynamical models and has much more skill in forecasting precipitation for June-September when heavy precipitation is more likely to occur than for May or October. Our approach enriches the knowledge of the relationship between precipitation and SSTA, which is conducive to the improvement of long-range precipitation forecasting.