Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan

Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.

Effects of Climate and Land Use changes on Vegetation Dynamics in the Yangtze River Delta, China Based on Abrupt Change Analysis

Vegetation dynamics is thought to be affected by climate and land use changes. However, how the effects vary after abrupt vegetation changes remains unclear. Based on the Mann-Kendall trend and abrupt change analysis, we monitored vegetation dynamics and its abrupt change in the Yangtze River delta during 1982–2016. With the correlation analysis, we revealed the relationship of vegetation dynamics with climate changes (temperature and precipitation) pixel-by-pixel and then with land use changes analysis we studied the effects of land use changes (unchanged or changed land use) on their relationship. Results showed that: (1) the Normalized Vegetation Index (NDVI) during growing season that is represented as GSN (growing season NDVI) showed an overall increasing trend and had an abrupt change in 2000. After then, the area percentages with decreasing GSN trend increased in cropland and built-up land, mainly located in the eastern, while those with increasing GSN trend increased in woodland and grassland, mainly located in the southern. Changed land use, except the land conversions from/to built-up land, is more favor for vegetation greening than unchanged land use (2) after abrupt change, the significant positive correlation between precipitation and GSN increased in all unchanged land use types, especially for woodland and grassland (natural land use) and changed land use except built-up land conversion. Meanwhile, the insignificant positive correlation between temperature and GSN increased in woodland, while decreased in the cropland and built-up land in the northwest (3) after abrupt change, precipitation became more important and favor, especially for natural land use. However, temperature became less important and favor for all land use types, especially for built-up land. This research indicates that abrupt change analysis will help to effectively monitor vegetation trend and to accurately assess the relationship of vegetation dynamics with climate and land use changes.

Spatiotemporal Variation and Abrupt Change Analysis of Temperature from 1960 to 2012 in the Huang-Huai-Hai Plain, China

Based on a monthly dataset of temperature time series (1960–2012) in the Huang-Huai-Hai Plain of China (HHHPC), spatiotemporal variation and abrupt change analysis of temperature were examined by moving average, linear regression, spline interpolation, Mann-Kendall test, and movingt-test. Major conclusions were listed as follows. (1) Annual and seasonal temperature increased with different rates on the process of fluctuating changes during 1960~2012. The upward trend was 0.22°C 10a−1for annual temperature, while it was very significant in winter (0.34°C 10a−1) and spring (0.31°C 10a−1), moderately significant in autumn (0.21°C 10a−1), and nonsignificant in summer (0.05°C 10a−1). (2) The spatial changes of annual and seasonal temperature were similar. The temperature increased significantly in Beijing and its adjacent regions, while it was nonsignificant in the central and southern regions. (3) The spring, autumn, winter, and annual temperature had warm abrupt change. The abrupt change time for winter temperature was in the late 1970s, while it was in the late 1980s and early 1990s for spring, autumn, and annual temperature. (4) Macroscopic effects of global and regional climate warming and human activities were probably responsible for the temperature changes. The climate warming would influence the hydrological cycle and agricultural crops in the study area.