Identification of Seasonal Sub-Regions of the Drought in the North China Plain

Abstract

Regional climate variability assessment is of great significance in decision-making such as agriculture and water resources system management. The identification of sub-regions with similar drought variability can provide a basis for agricultural disaster reduction planning and water resource distribution. In this research, a modified daily Standardized Precipitation Evapotranspiration Index (SPEI) was used to monitor the spatial and temporal variation characteristics of agricultural drought in the North China Plain from 1960 to 2017, which was studied by using the rotated empirical orthogonal functions (REOF). Through the seasonal REOF process, 7–9 seasonal drought sub-regions are confirmed by applying time series and the correlation relationship of SPEI original data. The strong correlation of these sub-regions indicates that the climate and weather conditions causing the drought are consistent and the drought conditions are independent for the regions that show no correlation. In general, the results of the seasonal trend analysis show that there has been no significant trend value in most areas since 1960. However, it is worth noting that some regions have the positive and negative temporal trends in different seasons. These results illustrate the importance of seasonal analysis, particularly for agro-ecosystems that depend on timely rainfall during different growing seasons. If this trend continues, seasonal drought will become more complex, then a more elaborate water management strategy will be needed to reduce its impact.

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

“…Owing to the vast territory and complex climate [15,61], the spatial-temporal pattern of droughts in China in the last few decades is far from fully understood [23]. Preceding studies focused on variations at the basin and local scales after the 1950s [18,59,60,[84][85][86], which poses a challenge to understanding the drywet variation in each region of China over long time periods [87]. It should be mentioned that in the published articles, the SPEI was not used as the drought index [21,43,73,[87][88][89].…”

“…In this sense, regional water balance is important for the selection of drought indices [34]. A recent study has discussed the applicability of the SPEI in China and found that it is better in humid and sub-humid areas than in arid areas [85]. This is because the ratio of ET anomaly (caused by temperature variation) in the surface water balance is relatively large in arid and semiarid regions with respect to humid regions [95].…”

“…When the duration of a drought is short, an annual drought index would exhibit its inherent limitations [87]. These descriptions illustrate the importance of seasonal analysis, in particular for agro-ecosystems that depend on timely rainfall during different growing seasons [85]. The importance of assessing and forecasting seasonal dry-wet variability and changes in other hydroclimate properties should be emphasized to provide more targeted information for the development of water resource management and drought mitigation strategies [141].…”

“…To better understand climate change and extreme events, further studies on the physical mechanism, especially attribution analyses at a regional scale, are urgently needed [131]. Natural variations in climate patterns and the influence of human activities have the potential to alter regional drought variability [85]. China has a large area and complex terrain [15,61], and the natural environment of the basins is also different in different administrative divisions [4].…”

“…How to quantitatively analyze the impact of the natural environment and human activities on drought deserves further study [4]. In addition, understanding the dynamic mechanisms of climate change [125] will contribute to understanding the difference of atmospheric physical processes that dominate regional drought variability [85]. Future studies on trend analysis could consider nonlinear models [39] that are predictive and also include the physical detail necessary to explain the processes behind the dry-wet variation (teleconnection) associations.…”