Abstract
Our research analyzes the regional changes of extreme dry spell, represented by the annual maximum dry spell length (noted as AMDSL) during the rainy season in the Wei River Basin (WRB) of China for 1960–2014 using the L-moments method. The mean AMDSL values increase from the west to the east of the WRB, suggesting a high dry risk in the east compared to the west in the WRB. To investigate the regional frequency more reasonably, the WRB is clustered into four homogenous subregions via theK-means method and some subjective adjustments. The goodness-of-fit test shows that the GEV, PE3, and GLO distribution can be accepted as the “best-fit” model for subregions 1 and 4, subregion 2, and subregion 3, respectively. The quantiles of AMDSL under various return levels figure out a similar spatial distribution with mean AMDSL. We also find that the dry risk in subregion 2 and subregion 4 might be higher than that in subregion 1. The relationship between ENSO events and extreme dry spell events in the rainy season with cross wavelet analysis method proves that ENSO events play a critical role in triggering extreme dry events during rainy season in the WRB.
Highlights
The Fifth Intergovernmental Panel on Climate Change (IPCC) report confirms a clear tendency of the probable occurrence of more extreme events in many regions around the world [1]
The main objectives of this study are to (1) divide the whole Wei River Basin (WRB) into several homogenous subregions, which are appropriate for the regional frequency analysis; (2) analyze the spatial variations of extreme dry spell and estimate the potential risk of the region which will be more prone to prolonged dry spells; (3) reveal the spatial variation of quantiles of AMDSL by the regional frequency method via L-moments method; (4) study the relationship between El Nino-Southern Oscillation (ENSO) events and extreme dry spells in the WRB using the cross wavelet analysis method
In order to explain the importance and significance of studying the dry spell during rainy season (RS), we firstly assess the proportion of the longest dry spell (LDS) in every year that occurred in RS, which can be computed as the total number of years exhibiting the LDS in RS divided by the total number of years in that station
Summary
The Fifth Intergovernmental Panel on Climate Change (IPCC) report confirms a clear tendency of the probable occurrence of more extreme events in many regions around the world [1]. As one of the costliest natural disasters through the agrosocioeconomic aspects in many areas of the world [4,5,6], droughts are expected to occur more frequently, caused by the predicted changes in the hydrologic cycle [7, 8]. They are supposed to largely affect the socioeconomic activities, water resources management, agriculture policies, and so forth [3]. It is worth noting that the drought frequency analysis using dry spell in many of the previous studies is on the basis of the single-site estimations
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