Identification and frequency analysis of drought–flood abrupt alternation events using a daily-scale standardized weighted average of the precipitation index

Abstract

Drought–flood abrupt alternation (DFAA), which is defined as the rapid transition between drought and flood in a short period, amplifies the negative impacts of individual drought or flood. DFAA is divided into drought to flood (DTF) and flood to drought (FTD) according to the sequences of drought and flood. Previous studies of identifying DFAA events have mostly been conducted over a long timescale (e.g., over a month), leading to inaccurate identification or omissions of DFAA events. In addition, frequency analysis of DTF and FTD events, which is vital for the design of hydraulic structures and water supply systems, has rarely been studied. This study establishes an identification method for DFAA events based on a daily-scale standardized weighted average of the precipitation index (SWAP) and conducts frequency analysis of DTF and FTD events based on copula theory. The Han River Basin (HRB), China, a crucial area for water resource management, was selected as the study area. Our findings disclose that DFAA events occurred less frequently with larger intensities and durations between 1961 and 2020. The temporal trends of DTF and FTD events varied similarly, while the number of DTF events were less than that of FTD events, indicating that FTD is the main performance of DFAA in HRB. In addition, the identification processes of DFAA events at the Wuhan station were analyzed in detail and proved that SWAP is an effective index for capturing the change of precipitation and accurately depicting the occurrences of droughts and floods. Furthermore, drought intensity (DI) and flood intensity (FI) were selected for bivariate frequency analysis. An OR case was defined as DI ≥ di or FI ≥ fi, while an AND case was defined as DI ≥ di and FI ≥ fi. The results of frequency analysis showed that joint return periods (JRPs) of DFAA events under the OR case are basically equal in three sub-basins, meaning a similar occurrence of probability of drought–flood disaster. JRPs under the AND case increased from the upper and middle basins to the lower basin as a whole, indicating the decreasing risk of abrupt drought–flood transitions. Overall, this study may have potential value in the early warning and mitigation of DFAA disaster.

Spatial-Temporal Variations of Drought-Flood Abrupt Alternation Events in Southeast China

“…Similar results can be found in [25]. Yang et al [26] revealed that the SWAP index, combined with the multi-threshold run theory enables a more accurate identification of DFAA events in the Yangtze River basin [27][28][29]. The study found that the SPI and SPEI have strong monitoring ability for drought in the southern part of northern China.…”

Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China

“…With the increasing buffer zone (10-15 km), the correlation between PD, the mean of slope change rate, mean slope, and minimum elevation increased. Compared with precipitation, the underlying causes of DW are geomorphic types, geology, terrain, and human activities [22]. These parameters frequently exhibit spatial heterogeneity on a larger scale and are much more closely related in the karst region, which in our study also coincides with the rising scale-dependent correlation of terrain [30].…”

“…The NDDWI could be improved in the future to better present the combined effects of DW or other extreme weather events or disasters. Using NDDWI in conjunction with other relevant indices, e.g., the drought-flood abrupt alternation (DFAA) index [22,59,60], can effectively demonstrate the impact of combined hazards.…”

“…Karst drought and flood studies are often based on data from meteorological stations [18], climatic datasets [19], and remote sensing images [20]. Multiple indexes are the most common methods used to monitor drought and flood events, e.g., SPI (standardized precipitation index) [18,21], SWAP (standardized weighted average of the precipitation index) [22], ETI (evapotranspiration index) [23], CDI (composite drought index) [24], China-Z index [19], etc. Systemic and hydrological models are also applied in karst aquifers without accurate knowledge regarding the physics and detailed geometry of the karst system, i.e., neural network models [25] and SWAT (the soil and water assessment tool) [7,26].…”